Inference by conditioning a generative model is also a basic building block of Bayesian statistics.
In cognitive science this tool can be used in two ways.
If the generative model is a hypothesis about a person’s model of the world, then we have a Bayesian *cognitive model* – the main topic of this book.
If the generative model is instead the scientist’s model of how the data are generated, then we have *Bayesian data analysis*.
Bayesian data analysis can be an extremely useful tool to us as scientists, when we are trying to understand what our data mean about psychological hypotheses.
This can become confusing: a particular modeling assumption can be something we hypothesize that people assume about the world, or can be something that we as scientists want to assume (but don’t assume that people assume).
A pithy way of saying this is that we can make assumptions about “Bayes in the head” (Bayesian cognitive models) or about “Bayes in the notebook” (Bayesian data analysis).

Coins, in their production, have a number of physical dimensions along which they vary. These idiosyncracies have no impact on the behavior of a coin when flipped. Flipping, it turns out, cross-cuts certain dimensions, and the probability that any modern coin in production will land on heads when flipped is roughly 0.5.

It turns out spinning a coin is quite different.
The heterogeneity of individual coins can be seen in their behavior when spun: The probability that any given coin will land on heads after being *spun* is not 0.5.
The probability that a given coin will spin to heads depends is complicated ways upon the idiosynracies of that coin.
(N.B. This knowledge is attributed to Persi Diaconis, Dept. of Statistics, Stanford, who M.H.T. was fortunate enough to hear describe his experiments on coins.)

Imagine I give you a coin, and want you to estimate the probability it will spin to heads.
Given what you know, the most reasonable prior belief is to expect *any* probability of the coin spinning to heads.
This can be captured in a uniform prior on , the probability that a coin when spun will land on heads: `var p = uniform(0,1)`

.

You conduct an experiment. You spin the coin 20 times. 15 of them, they spin to heads.

What if I paid you $1 if you could predict the next coin flip? What would you predict (Heads or Tails)? How much would you pay to take this bet? (Rule is: You have to offer at least $1. If you offered $1 for this bet, this would express agnosticism between “heads” and “tails”.)

```
var observerModel = function(){
var p = uniform(0, 1)
var coinSpinner = Binomial({n:20, p:p})
observe(coinSpinner, 15)
return p
}
var opts = {method: "rejection", samples: 5000}
var posteriorBeliefs = Infer(opts, observerModel)
print("Expected value = " + expectation(posteriorBeliefs))
viz.density(posteriorBeliefs, {bounds: [0,1]})
```

The model above is a hypothesis about how a person updates her prior beliefs about the probability of a coin being spun to heads, upon conducting 20 trials of a spinning experiment.
We can use this model to make predictions about other kinds of questions we could then ask the observer.
For instance, let’s take up the bet of whether or not the *next spin* will go to heads.
Also, consider if you were to make 10 more spins: How many of them would go to heads?

```
var observerModel = function(){
var p = uniform(0, 1)
var coinSpinner = Binomial({n:20, p:p})
observe(coinSpinner, 15)
return {
nextOutcome: bernoulli(p),
nextTenOutcomes: binomial(p, 10)
}
}
var opts = {method: "rejection", samples: 5000}
var posteriorBeliefs = Infer(opts, observerModel)
viz.marginals(posteriorBeliefs)
```

A model can be used to make predictions about different tasks. Models are useful in this way: They are tools for thinking through the implications of hypotheses.

Above, we have one hypothesis about a person thinking about the spinning coins experiments.
There are other hypotheses we could make about this experiment.
Rather than have uniform prior beliefs, maybe people port their knowledge of flipping behavior, and believe with some probability that spinning coins will behave similarly: There is a reasonable chance that spinning coins is as random as flipping coins.
But if that belief is not well supported, the observer can accomodate that by inferring that the behavior of spinning coins is quite different than that of flipping coins (as I explained above). If the observer beliefs the behavior of spinning coins is different than that of flipping coins, the model takes on the form of the model above: uniform beliefs about `p`

.
We’ll call this model the “skepticalModel” because it is, in a way, skeptical of the story I told you above about spinning coins.

```
var skepticalModel = function(){
var sameAsFlipping = flip(0.5)
var p = sameAsFlipping ? 0.5 : uniform(0, 1)
var coinSpinner = Binomial({n:20, p:p})
observe(coinSpinner, 15)
return {
sameAsFlipping: sameAsFlipping,
p: p,
nextOutcome: flip(p),
nextTenOutcomes: binomial(p, 10)
}
}
var opts = {method: "rejection", samples: 5000}
var posteriorBeliefs = Infer(opts, skepticalModel)
viz.marginals(posteriorBeliefs)
```

The predictions are subtly different.
`skepticalModel`

pulls the predictions more towards 50/50.
Why is that?
One way to understand this model is to example the prior.
Try commenting out the `observe`

statement and looking at the predictions.

The above models instantiate different hypotheses about the coin spinning experiemnts, and make different predictions about what people will do in this situation. We could recruit volunteers, run experiments in a laboratory, and record our participants’ responses. But, how are we to decide which model is better?

One way to begin to address this would be to ask ourselves: How much do we believe in each model? At this point, both models seem totally reasonable to me: I would say I believe in each of them equally. Okay, then how are we supposed to update these beliefs?

Since each model makes predictions about what we should observe in an experiment, we already know how to update our beliefs in these models based on the experimental data we observed.
We simply `observe`

the data, assuming that it was generated by the better model.
(If the data did *not* come from the better model, then the model that wasn’t the better model would be the better model.
That is, it’s to assume the data came from the better model.)

```
var scientistModel = function(){
var theBetterModel = flip(0.5) ? observerModel : skepticalModel
observe(theBetterModel, experimentalData)
return theBetterModel
}
```

We have now instantiated our scientific process as a probabilistic program, doing Bayesian inference. This is the foundation of Bayesian data analysis.

We must specify prior beliefs about which is the better model; in this case we say that we don’t have any bias in our prior beliefs: each model is equally likely to be better *a priori*.
We then seek to update our beliefs about which is the better model, by observing `experimentalData`

, assuming that it came from `theBetterModel`

.

Imagine we ran the “predict the next 10” experiment with 20 participants, and observed the following responses:

```
var experimentalData = [9,8,7,5,4,5,6,7,9,4,8,7,8,3,9,6,5,7,8,5]
```

Look again at the model predictions for `nextTenOutcomes`

for the two models above.
Which model do you think is better?

We are now ready to put all the pieces together:

```
///fold:
var opts = {method: "rejection", samples: 5000}
print("generating observer model predictions")
var observerModel = Infer(opts, function(){
var p = uniform(0, 1)
var coinSpinner = Binomial({n:20, p:p})
observe(coinSpinner, 15)
return binomial(p, 10)
})
print("generating skeptical model predictions")
var skepticalModel = Infer(opts, function(){
var sameAsFlipping = flip(0.5)
var p = sameAsFlipping ? 0.5 : uniform(0, 1)
var coinSpinner = Binomial({n:20, p:p})
observe(coinSpinner, 15)
return binomial(p, 10)
})
///
var experimentalData = [9,8,7,5,4,5,6,7,9,4,8,7,8,3,9,6,5,7,8,5]
// package the models up in an Object (for ease of reference)
var modelObject = {observerModel: observerModel, skepticalModel: skepticalModel};
var scientistModel = function(){
var theBetterModel_name = flip(0.5) ? "observerModel" : "skepticalModel"
var theBetterModel = modelObject[theBetterModel_name]
map(function(d){ observe(theBetterModel, d) }, experimentalData)
return {betterModel: theBetterModel_name}
}
var modelPosterior = Infer({method: "enumerate"}, scientistModel)
viz(modelPosterior)
```

Bayesian data analysis is a general purpose data analysis approach for making explicit hypotheses about the generative process behind the experimental data (i.e., how was the experimental data generated? e.g., the hypothesis that data from two experimental conditions came from two different distributions).
After making explicit hypotheses, Bayesian inference can be used to *invert* the model: go from experimental data to updated beliefs about the hypotheses.

For further reading on Bayesian data analysis: see Lee & Wagenmakers (2013), Kruschke (2014), and Gelman et al. (2014).

Models have parameters. Parameters can be of theoretical interest or not (so-called, nuisance parameters). Learning about a hypothesis involves inferences based on the values of parameters. For example, if you’re trying to estimate the proportion of eligible people who signed up for health insurance after the signing of the Affordable Care Act (ObamaCare), and you find we that there is a 95% chance that between 81-86% of eligible people (people without health insurance before) signed up, then you might be tempted to draw a conclusion about the efficacy. (Disclaimer: I have no idea what the true numbers for that example are.)

Parameters are in general unobservable. Trying to estimate how many (voting age, likely to vote) people prefer Candidate A vs. Candidate B would require asking over 100 million people (it’s estimated that about 130 million people voted in the US Presidential Elections in 2008 and 2012). It’s impractical to measure the whole distribution. Instead, what is done is measuring a sample (maybe we ask 1000 people), and use that to make inference about the “true population proportion” (an unobservable parameter).

Bayes’ rule provides a bridge between the unobserved parameters of models and the observed data. We can update our beliefs about parameters from data. Additionally, the “Bayes bridge” can go from parameters to data: we can use our updated beliefs about a parameter to make predictions about future data sets.

For a given Bayesian model (together with data), there are four conceptually distinct distributions of interest:

For parameters, we have priors and posteriors:

- The
*prior distribution over parameters*captures our initial state of knowledge (or, our beliefs) about the latent variables those parameters represent. - The
*posterior distribution over parameters*captures what we know about the latent variables having updated our beliefs with the evidence provided by data.

We can run either the prior or the posterior model forward, and have it make predictions about data sets:

- The
*prior predictive distribution*tells us what data to expect, given our model and our initial beliefs about the parameters. The prior predictive is a distribution over data, and gives the relative probability of different*observable*outcomes before we have seen any data. - The
*posterior predictive distribution*tells us what data to expect, given the same model we started with, but with beliefs that have been updated by the observed data. The posterior predictive is a distribution over data, and gives the relative probability of different observable outcomes, after some data has been seen.

Loosely speaking, *predictive* distributions are in “data space” and *parameter* distributions are in “latent space”.

Here, we explore the result of an experiment with 20 trials and binary outcomes (e.g., asking people if they’ll vote for Candidate A or Candidate B, …). Note that is the same model as the “observerModel” above.

```
// observed data
var k = 1 // number of people who support candidate A
var n = 20 // number of people asked
var model = function() {
// true population proportion who support candidate A
var p = uniform(0, 1);
// Observed k people support "A"
// Assuming each person's response is independent of each other
observe(Binomial({p : p, n: n}), k);
// predict what the next n will say
var posteriorPredictive = binomial(p, n);
// recreate model structure, without observe
var prior_p = uniform(0, 1);
var priorPredictive = binomial(prior_p, n);
return {
prior: prior_p, priorPredictive : priorPredictive,
posterior : p, posteriorPredictive : posteriorPredictive
};
}
var opts = {method: "rejection", samples: 2000};
var posterior = Infer(opts, model);
viz.marginals(posterior)
```

Try to interpret each plot, and how they relate to each other.
Why are some plots densities and others bar graphs?
Understanding these ideas is a key to understanding Bayesian analysis.
Check your understanding by trying other data sets, varying both `k`

and `n`

.

The posterior predictive distribution describes what data you should expect to see, given the model you’ve assumed and the data you’ve collected so far. If the model is a good description of the data you’ve collected, then the model shouldn’t be surprised if you got the same data by running the experiment again. That is, the most likely data for your model after observing your data should be the data you observed.

It’s natural then to use the posterior predictive distribution to examine the descriptive adequacy of a model.
If these predictions do not match the data *already seen* (i.e., the data used to arrive at the posterior distribution over parameters), the model is descriptively inadequate.

Imagine you’re a developmental psychologist, piloting a two-alternative forced choice task on young children. (Just for fun, let’s pretend it’s a helping study, where the child either chooses to help or not help a confederate in need.) You have two research assistants that you send to two different preschools to collect data. You got your first batch of data back today: For one of your research assistants, 10 out of 10 children tested helped the confederate in need. For the other research assitant, 0 out of 10 children tested helped.

We’ll use the `editor.put()`

function to save our results so we can look at the them in different code boxes.

```
///fold:
var marginalize = function(dist, key){
return Infer({method: "enumerate"}, function(){
return sample(dist)[key]
})
}
///
// "Kids who help" in 2 experiments
var k1 = 0;
var k2 = 10;
// Number of kids in 2 experiments
var n1 = 10;
var n2 = 10;
var model = function() {
// "true effect in the population"
var p = uniform(0, 1);
// observed data from 2 experiments
observe(Binomial({p: p, n: n1}), k1);
observe(Binomial({p: p, n: n2}), k2);
// posterior prediction
var posteriorPredictive1 = binomial(p, n1)
var posteriorPredictive2 = binomial(p, n2)
return {
parameter : p,
predictive: {
predictive1: posteriorPredictive1,
predictive2: posteriorPredictive2
}
};
}
var opts = {
method: "MCMC", callbacks: [editor.MCMCProgress()],
samples: 20000, burn: 10000
};
var posterior = Infer(opts, model);
var posteriorPredictive = marginalize(posterior, "predictive")
// save results for future code boxes
editor.put("posteriorPredictive", posteriorPredictive)
var parameterPosterior = marginalize(posterior, "parameter")
viz.density(parameterPosterior, {bounds: [0, 1]})
```

Looks like a reasonable posterior distribution.

How does the posterior predictive look?

```
var posteriorPredictive = editor.get("posteriorPredictive")
viz(posteriorPredictive)
```

How well does it recreate the observed data? Where in this 2-d grid would our observed data land?

Another way of visualizing the model-data fit is to examine a scatterplot.

```
var k1 = 0, k2 = 10;
var posteriorPredictive = editor.get("posteriorPredictive")
var posteriorPredictiveMAP = posteriorPredictive.MAP().val
viz.scatter(
[
{model: posteriorPredictiveMAP.predictive1, data: k1},
{model: posteriorPredictiveMAP.predictive2, data: k2}
]
)
```

Think about the posterior predictive fit.
What can you conclude about the parameter `p`

?

In the above examples, we’ve had a single data-analysis model and used the experimental data to learn about the parameters of the models and the descriptive adequacy of the models.
Often as scientists, we are in fortunate position of having multiple, distinct models in hand, and want to decide if one or another is a better description of the data.
Indeed, we saw an example above when we decided whether `"observerModel"`

or `"skepticalModel"`

was a better explanation of some (made-up) data.
In that example, model comparison was shown a special case of learning about the parameters of a model.
In that case, we defined an uber model (`scientistModel`

), that had a binary decision parameter that we wanted to learn about (which one of the models was better).
We did this by having the binary decision variable gate between which of our two models we let generate the data.
We then go backwards (performing Bayesian inference) to decide which model was more likely to have generated the data we observed.

We take the same approach here, articulating the simplest data analysis model for model comparison. We observe some number of binary outcomes and want to decide if the pattern we see is random or not (or to see, in the parlance of scientists, whether something systematic is going on; this model mimics a simple cognitive model for subjective randomness, which we will explore in a later chapter).

Formally, a “true random” pattern would be generated by a coin with probability of doing one or other binary outcome as 0.5 (a fair coin); a “non random” pattern would be generated by a trick coin.
We run into our first complication: A fair coin is simple to define: `Bernoulli({p: 0.5})`

, but how can we treat a trick coin?
For purposes of scientific hypothesis testing, a trick coin is formalized as a coin with some *unknown* weight:

```
var p = uniform(0,1);
Bernoulli({p: p})
```

Using this to now compare models:

```
var k = 7, n = 20;
var compareModels = function() {
// binary decision variable for which hypothesis is better
var x = flip(0.5) ? "simple" : "complex";
var p = (x == "simple") ? 0.5 : uniform(0, 1);
observe(Binomial({p: p, n: n}), k);
return {model: x}
}
var opts = {method: "rejection", samples: 2000};
print("We observed " + k + " successes out of " + n + " attempts")
var modelPosterior = Infer(opts, compareModels);
viz(modelPosterior)
```

This model is an example from the classical hypothesis testing framework.
We consider a model that fixes one of its parameters to a pre-specified value of interest (here ).
This is sometimes referred to as a *null hypothesis*.
The other model says that the parameter is free to vary.
In the classical hypothesis testing framework, we would write: .
With Bayesian hypothesis testing, we must be explicit about what is (not just what p is not), so we write .

One might have a conceptual worry: Isn’t the second model just a more general case of the first model?
That is, if the second model has a uniform distribution over `p`

, then `p: 0.5`

is included in the second model.
This is what’s called a *nested model*.

Shouldn’t the more general model always be better? If we’re at a track, and you bet on horse A, and I bet on horse A and B, aren’t I strictly in a better position than you? The answer is no, and the reason has to do with our metric for winning. Intuitively, we don’t care whether your horse won or not, but how much money you win. How much money you win depends on how much money you bet, and the rule is, when we go to track, we have the same amount of money. (Intuitively, our money is probability, and we each have a distribution to bet; a distribution has probabilities that must must sum to 1.) So my act of betting on horse A and horse B actually requires me to split my money (say, betting 50 / 50 on each). On the other hand, you put all your money on horse A (100 on A, 0 on B). If A wins, you will gain more money because you put more money down.

This idea is called the principle of parsimony or Occam’s razor, and will be discussed at length later in this book. For now, it’s sufficient to know that more complex models will be penalized for being more complex intuitively because they will be diluting their predictions. At the same time, more complex models are more flexible and can capture a wider variety of data. Bayesian model comparison lets us weigh these costs and benefits.

What we are plotting above are **posterior model probabilities**.
These are a function of the marginal likelihoods of the data under each hypothesis and the prior model probabilities (here, defined to be equal: `flip(0.5)`

).
Sometimes, scientists feel a bit strange about reporting values that are based on prior model probabilities (what if scientists have different priors as to the relative plausibility of the hypotheses?) and so often report the ratio of marginal likelihoods, a quantity known as a *Bayes Factor*.

Let’s compute the Bayes’ Factor, by computing the likelihood of the data under each hypothesis.

```
var k = 7, n = 20;
var simpleLikelihood = Math.exp(Binomial({p: 0.5, n: n}).score(k))
var complexModel = Infer({method: "forward", samples: 10000}, function(){
var p = uniform(0, 1);
return binomial(p, n)
})
var complexLikelihood = Math.exp(complexModel.score(k))
var bayesFactor_01 = simpleLikelihood / complexLikelihood
bayesFactor_01
```

How does the Bayes Factor in this case relate to posterior model probabilities above?

For this example, the Bayes factor can be obtained by integrating out the model parameter (using `Infer`

with `{method: "forward"}`

).
However, it is not always easy to get good estimates of the two marginal probabilities.
It turns out, the Bayes factor can also be obtained by considering *only* the more complex hypothesis ().
What you do is look at the distribution over the parameter of interest (here, ) at the point of interest (here, ).
Dividing the probability density of the posterior by the density of the prior (of the parameter at the point of interest) also gives you the Bayes Factor!
This perhaps surprising result was described by Dickey and Lientz (1970), and they attribute it to Leonard “Jimmie” Savage.
The method is called the *Savage-Dickey density ratio* and is widely used in experimental science.

We would use it like so:

```
var k = 7, n = 20;
var complexModelPrior = Infer({method: "forward", samples: 10000}, function(){
var p = uniform(0, 1);
return p
})
var complexModelPosterior = Infer({method: "rejection", samples: 10000}, function(){
var p = uniform(0, 1);
observe(Binomial({p: p, n: n}), k);
return p
})
var savageDickeyDenomenator = expectation(complexModelPrior, function(x){return Math.abs(x-0.5)<0.05})
var savageDickeyNumerator = expectation(complexModelPosterior, function(x){return Math.abs(x-0.5)<0.05})
var savageDickeyRatio = savageDickeyNumerator / savageDickeyDenomenator
print( savageDickeyRatio )
```

(Note that we have approximated the densities by looking at the expectation that is within of the target value .)

One of the virtues of Bayesian data analysis is it’s ability to interface with Bayesian models of cognition in a natural way. Bayesian cognitive models are formalizations of hypotheses about cognition, which we then can test with an experiment. We can contrast our rich Bayesian cognitive models with more standard models from data science, like linear regression, and evaluate them all using Bayesian data analysis.

Regression is the workhorse of data science. Regression models are useful in situations when you have (1) data and (2) some (potentially vague) hypotheses about how variables relate to each other (e.g., that demographics might predict political party affiliation [in some unspecified way]). In psychology and many other behavioral sciences, experiments are often constructed with discrete/categorical manipulations (e.g., measuring processing time of words vs. pseudowords). The question “is A greater than B?” (is the processing time of words faster than the processing time of pseudowords?) can be answered using a regression model.

To explore a Bayesian linear regression model, we will use data from the Tug-of-War experiment by @Gerstenberg2012.
Let’s be good data scientists, and start by just taking a look at the data set, found in the `towData`

variable (available in this page only).

```
var levels = function(a, lvl){ return _.uniq(_.pluck(a, lvl)) }
// display single row of the data frame
print(towData[0])
// display unique levels of "pattern" variable
print(levels(towData, "pattern"))
// display unique levels of "tournament" variable
print(levels(towData, "tournament"))
// display unique levels of "nWins" variable
print(levels(towData, "nWins"))
// display unique levels of "id" variable [participant id]
print(levels(towData, "id"))
```

The first line printed is a line from our data set: one participant on one trial. We see that it has many different fields, including the trial number, their raw rating (“rating”), a normalized score (“ratingZ”), and information about the experimental condition. Here, this was the condition “confounded evidence” in a “singles” tournament: here, the target player won 3 times against the same player (for a full list of the experimental conditions see @Gerstenberg2012 Tables 2 and 3).

The other lines show the unique values different variables can take on.

Let’s plot the `ratingZ`

variable (a normalized rating).

```
viz.hist(_.pluck(towData, "ratingZ"))
```

This distribution of ratings is from all trials, all participants, all experimental conditions. We see that the ratings range from about -2 to 2. The most likely ratings are one standard deviation above or below the mean, though some ratings are at the mean of 0.

Let’s say we ran this experiment and hypothesized that the number of times the target character won (`"nWins"`

in the data set) is a predictor of how participants’ ratings of strength.
We’ll formalize this in a Bayesian regression framework, where ratings of strength are a linear combination of a fixed intercept and weighted component of number of wins .

Because we’re in the business of building generative models, we will have to be explicit about how relates to the actual rating data we observed. We make the standard assumption that the actual ratings are normally distributed around , with some noise . [This is analagous to having “randomly distributed errors”.]

This is a model of our data. As in cognitive models, we will put priors on the parameters: , and infer their likely values by conditioning on the observed data.

```
// alternative proposal distribution for metropolis-hastings algorithm
var uniformKernel = function(prevVal) {
return Uniform({a: prevVal - 0.2, b: prevVal + 0.2});
};
var singleRegression = function(){
// parameters of a simple linear regression
var b0 = sample(Uniform({a: -1, b: 1}), {driftKernel: uniformKernel})
var b1 = sample(Uniform({a: -1, b: 1}), {driftKernel: uniformKernel})
var sigma = sample(Uniform({a: 0, b: 2}), {driftKernel: uniformKernel})
map(function(d){
// linear regression formula
var predicted_y = b0 + d.nWins*b1
observe(Gaussian({mu: predicted_y, sigma: sigma}), d.ratingZ)
}, towData)
return {b0: b0, b1: b1, sigma: sigma}
}
var nSamples = 2500
var opts = { method: "MCMC", callbacks: [editor.MCMCProgress()],
samples: nSamples, burn: nSamples/2 }
var posterior = Infer(opts, singleRegression)
editor.put("singleRegression", posterior)
```

```
var posterior = editor.get("singleRegression")
viz.marginals(posterior)
```

The posteriors are somewhat noisy because we haven’t taken that many samples. We see that the intercept is around 0, which we might expect given that our data is normalized. The slope weight is around 0.35, with relatively low variance around that. The fact that it’s very unlikely for to be 0 suggests that there is an effect of the number of times the actor has won in Tug of War on participants’ judgments of the relative strength of that actor, as we might hope. is almost around 0.5, which seems a little bit high given that the full range of the response ratings is 4 (-2 to +2).

We can now critique the model by asking how well it would generate our data. To do this, we look at the posterior predictive distribution. There are 20 different experimental conditions (wins vs. loss, singles vs. doubles, and 4 - 6 different kinds of tournaments). We want to examine our predictions for each of these conditions separately, so we rewrite the model slightly by mapping over each condition variable separately.

```
var merge = function(m, d){
var keys = _.keys(d)
map(function(k){return {model: m[k], data: d[k], item:k} }, keys)
}
var levels = function(a, lvl){ return _.uniq(_.pluck(a, lvl)) }
var outcomes = levels(towData, "outcome");
var tournaments = levels(towData, "tournament");
var patterns = {
single: levels(_.where(towData, {tournament: "single"}), "pattern"),
double: levels(_.where(towData, {tournament: "double"}), "pattern")
};
// alternative proposal distribution for metropolis-hastings algorithm
var uniformKernel = function(prevVal) {
return Uniform({a: prevVal - 0.2, b: prevVal + 0.2});
};
var singleRegression = function(){
var b0 = sample(Uniform({a: -1, b: 1}), {driftKernel: uniformKernel})
var b1 = sample(Uniform({a: -1, b: 1}), {driftKernel: uniformKernel})
var sigma = sample(Uniform({a: 0, b: 2}), {driftKernel: uniformKernel})
var predictions = map(function(tournament){
return map(function(outcome){
return map(function(pattern){
var itemInfo = {pattern: pattern, tournament: tournament, outcome: outcome}
var itemData = _.where(towData, itemInfo)
// linear regression formula
var predicted_y = b0 + itemData[0]["nWins"]*b1
map(function(d){ observe(Gaussian({mu: predicted_y, sigma: sigma}), d.ratingZ)}, itemData)
return _.object([[pattern + "_" + tournament + "_" + outcome, predicted_y]])
}, patterns[tournament]) // singles tournaments don't have all patterns
}, outcomes)
}, tournaments)
// nasty data munging
return _.object(_.flatten(map(function(i){ _.pairs(i) }, _.flatten(predictions)), true))
}
var nSamples = 500
var opts = { method: "MCMC", callbacks: [editor.MCMCProgress()],
samples: nSamples, burn: nSamples/2 }
var posterior = Infer(opts, singleRegression)
var modelDataDF = merge(posterior.MAP().val, towMeans)
viz.scatter(modelDataDF)
editor.put('singleRegression', posterior)
editor.put('modelDataDF', modelDataDF)
```

```
///fold:
var correlation = function(xs, ys) {
var mx = sum(xs)/xs.length,
my = sum(ys)/ys.length;
var num = sum(map2(function(x,y) { (x-mx) * (y-my)}, xs, ys));
var den = Math.sqrt(sum(map(function(x) { (x-mx) * (x-mx)},xs))) *
Math.sqrt(sum(map(function(y) { (y-my) * (y-my)},ys)));
return num/den
}
///
var modelDataDF = editor.get('modelDataDF')
var summaryData = map(function(x){
return _.extend(x, {sqErr: Math.pow(x.model-x.data, 2)})
}, modelDataDF)
print("Mean squared error = " + listMean(_.pluck(summaryData, "sqErr")))
var varianceExplained = Math.pow(correlation(_.pluck(summaryData, "data"), _.pluck(summaryData, "model")), 2)
print("Model explains " + Math.round(varianceExplained*100) + "% of the data")
viz.table(summaryData)
```

The simple linear regression does surprisingly well on this data set (at least at predicting the mean responses). This is important to know; it provides a standard against which we can evaluate richer models.

At the same time, we observe in the posterior predictive scatterplot that not all the linear model is predicting certain symmetries that don’t come out. Why might that be?

Now, some of the conditions has Alice winning against the same person, so maybe it’s also important how many unique wins she has.

```
///fold:
var levels = function(a, lvl){ return _.uniq(_.pluck(a, lvl)) }
var outcomes = levels(towData, "outcome");
var tournaments = levels(towData, "tournament");
var patterns = {
single: levels(_.where(towData, {tournament: "single"}), "pattern"),
double: levels(_.where(towData, {tournament: "double"}), "pattern")
};
// alternative proposal distribution for metropolis-hastings algorithm
var uniformKernel = function(prevVal) {
return Uniform({a: prevVal - 0.2, b: prevVal + 0.2});
};
///
var multipleRegression = function(){
var b0 = sample(Uniform({a: -1, b: 1}), {driftKernel: uniformKernel})
var b1 = sample(Uniform({a: -1, b: 1}), {driftKernel: uniformKernel})
var b2 = sample(Uniform({a: -1, b: 1}), {driftKernel: uniformKernel})
var sigma = sample(Uniform({a: 0, b: 2}), {driftKernel: uniformKernel})
var predictions = map(function(tournament){
return map(function(outcome){
return map(function(pattern){
var itemInfo = {pattern: pattern, tournament: tournament, outcome: outcome}
var itemData = _.where(towData, itemInfo)
// linear equation
var predicted_y = b0 + itemData[0]["nWins"]*b1 + itemData[0]["nUniqueWins"]*b2
map(function(d){ observe(Gaussian({mu: predicted_y, sigma: sigma}), d.ratingZ) }, itemData)
return _.object([[pattern + "_" + tournament + "_" + outcome, predicted_y]])
}, patterns[tournament]) // singles tournaments don't have all patterns
}, outcomes)
}, tournaments)
return {
parameters: {b0: b0, b1: b1, b2: b2, sigma: sigma},
predictives: _.object(_.flatten(map(function(i){ _.pairs(i) }, _.flatten(predictions)), true))
}
}
var nSamples = 250
var opts = { method: "MCMC", kernel: {HMC: {steps: 5, stepSize: 0.01}},
callbacks: [editor.MCMCProgress()],
samples: nSamples, burn: nSamples/2 }
var posterior = Infer(opts, multipleRegression)
editor.put('multiRegression', posterior)
```

Look at parameters.

```
///fold:
var marginalize = function(dist, key){
return Infer({method: "enumerate"}, function(){
return sample(dist)[key];
})
}
///
var posterior = editor.get('multiRegression');
var parameterPosterior = marginalize(posterior, "parameters")
viz.marginals(parameterPosterior)
```

We see that is also probably not 0, suggesting that the number of *unique* wins a player has is relavent for predicting participants’ judgments of their strength.
How well does the model fit the data?

```
///fold:
var marginalize = function(dist, key){
return Infer({method: "enumerate"}, function(){
return sample(dist)[key];
})
}
var merge = function(m, d){
var keys = _.keys(d)
return map(function(k){return {model: m[k], data: d[k], item:k} }, keys)
}
var correlation = function(xs, ys) {
var mx = sum(xs)/xs.length,
my = sum(ys)/ys.length;
var num = sum(map2(function(x,y) { (x-mx) * (y-my)}, xs, ys));
var den = Math.sqrt(sum(map(function(x) { (x-mx) * (x-mx)},xs))) *
Math.sqrt(sum(map(function(y) { (y-my) * (y-my)},ys)));
return num/den
}
///
var posterior = editor.get('multiRegression');
var posteriorPredictive = marginalize(posterior, "predictives")
var modelDataDF = merge(posteriorPredictive.MAP().val, towMeans)
var summaryData = map(function(x){
return _.extend(x, {sqErr: Math.pow(x.model-x.data, 2)})
}, modelDataDF)
print("Mean squared error = " + listMean(_.pluck(summaryData, "sqErr")))
var varianceExplained = Math.pow(correlation(_.pluck(summaryData, "data"), _.pluck(summaryData, "model")), 2)
print("Model explains " + Math.round(varianceExplained*100) + "% of the data")
viz.scatter(modelDataDF)
viz.table(summaryData)
```

The multiple linear regression model fit is improved a little bit, but still fails to predict meaningful difference between certain conditions.

With regressions like these, we’re often asking binary questions (e.g., “is this parameter 0 or not?”). These kinds of questions provide just a few bits of information. Instantiating a hypothesis in a cognitive model can answer more than just categorical questions by testing a richer theory of the data.

Recall the Tug-of-war model from the chapter on conditioning.

```
var options = {method: 'rejection', samples: 1000}
var lazinessPrior = 0.3;
var lazyPulling = 0.5;
var model = function() {
var strength = mem(function(person){
return gaussian(0, 1)
})
var lazy = function(person){
return flip(lazinessPrior)
}
var pulling = function(person) {
return lazy(person) ?
strength(person) * lazyPulling :
strength(person)
}
var totalPulling = function(team){return sum(map(pulling, team)) }
var winner = function(team1, team2){
totalPulling(team1) > totalPulling(team2) ? team1 : team2
}
var beat = function(team1,team2){winner(team1,team2) == team1}
condition(beat(["bob", "mary"], ["tom", "sue"]))
return strength("bob")
}
var posterior = Infer(options, model)
print("Bob's strength, given that he and Mary beat Tom and Sue")
print("Expected value = " + expectation(posterior))
viz(posterior)
```

To learn more about (and test) the tug-of-war model, we’re going to connect it to the data from the experiment.
You’ll notice that we have two parameters in this model: the proportion of a person’s strength they pull with when they are being lazy (`lazyPulling`

) and the prior probability of a person being lazy (`lazinessPrior`

).
Above, we set these parameters to be `0.5`

and `0.3`

, respectively.
People are lazy about a third of the time, and when they are lazy, they pull with half their strength.
(Technical note: Because we are comparing relative strengths and we have normalized the human ratings, we don’t have to infer the parameters of the gaussian in `strength`

.
We just use the standard normal distribution.)

Those parameter values aren’t central to our hypothesis. They are peripheral details to the larger hypothesis which is that people reason about team games like Tug of War by running a structured, generative model in their heads and doing posterior inference. Rather than guessing at what values we should put for these parameters, we can use the data to inform our beliefs about what those parameters are likely to be (assuming the general model is a good one).

```
///fold:
var levels = function(a, lvl){ return _.uniq(_.pluck(a, lvl)) }
var outcomes = levels(towData, "outcome");
var tournaments = levels(towData, "tournament");
var patterns = {
single: levels(_.where(towData, {tournament: "single"}), "pattern"),
double: levels(_.where(towData, {tournament: "double"}), "pattern")
};
var round = function(x){
return Math.round(x*10)/10
}
var bins = map(round, _.range(-2.2, 2.2, 0.1))
// alternative proposal distribution for metropolis-hastings algorithm
var lazinessPriorKernel = function(prevVal) {
return Uniform({a: prevVal - 0.1, b: prevVal + 0.1});
};
var lazyPullingKernel = function(prevVal) {
return Uniform({a: prevVal - 0.2, b: prevVal + 0.2});
};
///
// add a tiny bit of noise, and make sure every bin has at least epsilon probability
var smoothToBins = function(dist, sigma, bins){
Infer({method: "enumerate"}, function(){
var x = sample(dist);
var smoothedProbs = map(function(b){return Number.EPSILON+Math.exp(Gaussian({mu: x, sigma: sigma}).score(b)) }, bins)
return categorical(smoothedProbs, bins)
})
}
var tugOfWarOpts = {method: "rejection", samples: 500}
var tugOfWarModel = function(lazyPulling, lazinessPrior, matchInfo){
Infer(tugOfWarOpts, function(){
var strength = mem(function(person){
return gaussian(0, 1)
})
var lazy = function(person){
return flip(lazinessPrior)
}
var pulling = function(person) {
return lazy(person) ?
strength(person) * lazyPulling :
strength(person)
}
var totalPulling = function(team){return sum(map(pulling, team)) }
var winner = function(team1, team2){
totalPulling(team1) > totalPulling(team2) ? team1 : team2
}
var beat = function(team1,team2){winner(team1,team2) == team1}
condition(beat(matchInfo.winner1, matchInfo.loser1))
condition(beat(matchInfo.winner2, matchInfo.loser2))
condition(beat(matchInfo.winner3, matchInfo.loser3))
return round(strength("A"))
})
}
var dataAnalysisModel = function(){
var lazinessPrior = sample(Uniform({a: 0, b: 0.5}), {driftKernel: lazinessPriorKernel})
var lazyPulling = sample(Uniform({a: 0, b: 1}), {driftKernel: lazyPullingKernel})
var predictions = map(function(tournament){
return map(function(outcome){
return map(function(pattern){
var itemInfo = {pattern: pattern, tournament: tournament, outcome: outcome}
// participants' ratings
var itemData = _.where(towData, itemInfo)
// information about the winners and losers
var matchInformation = _.where(matchConfigurations, itemInfo)[0]
var modelPosterior = tugOfWarModel(lazyPulling, lazinessPrior, matchInformation)
var smoothedPredictions = smoothToBins(modelPosterior, 0.05, bins)
map(function(d){ observe(smoothedPredictions, d.roundedRating) }, itemData)
return _.object([[pattern + "_" + tournament + "_" + outcome, expectation(modelPosterior)]])
}, patterns[tournament]) // singles tournaments don't have all patterns
}, outcomes)
}, tournaments)
return {
parameters: {lazinessPrior: lazinessPrior, lazyPulling: lazyPulling},
predictives: _.object(_.flatten(map(function(i){ _.pairs(i) }, _.flatten(predictions)), true))
}
}
var nSamples = 20
var opts = { method: "MCMC",
callbacks: [editor.MCMCProgress()],
samples: nSamples, burn: 0 }
var posterior = Infer(opts, dataAnalysisModel)
editor.put("bda_bcm", posterior)
```

Look at parameters.

```
///fold:
var marginalize = function(dist, key){
return Infer({method: "enumerate"}, function(){
return sample(dist)[key];
})
}
///
var posterior = editor.get('bda_bcm');
var parameterPosterior = marginalize(posterior, "parameters")
viz.marginals(parameterPosterior)
```

Critique posterior predictive

```
///fold:
var marginalize = function(dist, key){
return Infer({method: "enumerate"}, function(){
return sample(dist)[key];
})
}
var merge = function(m, d){
var keys = _.keys(d)
return map(function(k){return {model: m[k], data: d[k], item:k} }, keys)
}
var correlation = function(xs, ys) {
var mx = sum(xs)/xs.length,
my = sum(ys)/ys.length;
var num = sum(map2(function(x,y) { (x-mx) * (y-my)}, xs, ys));
var den = Math.sqrt(sum(map(function(x) { (x-mx) * (x-mx)},xs))) *
Math.sqrt(sum(map(function(y) { (y-my) * (y-my)},ys)));
return num/den
}
///
var posterior = editor.get('bda_bcm');
var posteriorPredictive = marginalize(posterior, "predictives")
var modelDataDF = merge(posteriorPredictive.MAP().val, towMeans)
var summaryData = map(function(x){
return _.extend(x, {sqErr: Math.pow(x.model-x.data, 2)})
}, modelDataDF)
print("Mean squared error = " + listMean(_.pluck(summaryData, "sqErr")))
var varianceExplained = Math.pow(correlation(_.pluck(summaryData, "data"), _.pluck(summaryData, "model")), 2)
print("Model explains " + Math.round(varianceExplained*100) + "% of the data")
viz.scatter(modelDataDF)
viz.table(summaryData)
```

An extended analysis of the Tug of War model (using RWebPPL) can be found here.

Test your knowledge: Exercises