Introduction

The presentation here is based on the following resources:

Simple Start

Goal: If U1 and U2 are two independent variables with uniform distribution over the integers 1, 2, and 3, what is the distribution of U1 == U2 ?

var main = function () {

var u1 = categorical({vs: [1,2,3]})
var u2 = categorical({vs: [1,2,3]})

return u1 == u2
}

var dist = Infer(
{method: 'enumerate'},
main);

viz.table(dist);
//viz.auto(dist);

Several Simple Models (AgentModels)

We will now look at several models from here

Simple Rate Inference (BCM Book)

Goal: infer the underlying success rate for a binary process. We want to determine the posterior distribution of the rate theta.

var main = function () {
var n = 10 //100
var theta = beta(1,1)

var k = binomial(theta, n)

condition (k == 5)  //50

return theta
}

var dist = Infer(
{method: 'MCMC', samples: 10000},
main);
viz.auto(dist);

Difference Between Two Rates (BCM Book)

Goal: We want to compare two different binomial processes.

var main = function () {
var n1 = 10
var n2 = 10
var theta1 = beta(1,1)
var theta2 = beta(1,1)

var k1 = binomial(theta1, n1)
var k2 = binomial(theta2, n2)

condition (k1 == 5)
condition (k2 == 7)

var delta = theta1 - theta2

return delta
}

var dist = Infer(
{method: 'MCMC', samples: 10000},
main);

viz.auto(dist);

// also: return [theta1, theta2]
// ...
//var dist = Infer(
//  {method: 'MCMC', samples: 1000},
//  main);

//viz.auto(dist);
//viz.marginals(dist);

Inferring a Common Rate (BCM Book)

Goal: infer the underlying success rate for a binary process. We want to determine the posterior distribution of the rate theta.

var main = function () {
var n1 = 10
var n2 = 10
var theta = beta(1,1)

var k1 = binomial(theta, n1)
var k2 = binomial(theta, n2)

condition (k1 == 5)
condition (k2 == 7)

return theta
}

var dist = Infer(
{method: 'MCMC', samples: 10000},
main);

viz.auto(dist);

Inferring Mean and Variance (BCM)

Warmup: factor vs condition

var dist = Infer({method: 'MCMC', samples: 10000},
function () { return gaussian(0,1) } );
viz.auto(dist)

var xs = [0, 0.1, -0.1, 0.2, -0.2, 0.02];

var main = function () {
var mu = gaussian(0, 3)

var XDist = Gaussian({mu: mu, sigma: 1})
observe (XDist, xs) // factor(XDist.score(xs));
observe (XDist, xs)
observe (XDist, xs)
observe (XDist, xs)
observe (XDist, xs)
observe (XDist, xs)

return mu
}

var dist = Infer({method: 'MCMC', samples: 10000},
main );
viz.auto(dist);

The score function computes the log-probability of a distribution:

print("Score of val = 0 for N(0,1): " + Math.exp(Gaussian({mu: 0, sigma: 1}).score(0)) )
print("Score of val = 1 for N(0,1): " + Math.exp(Gaussian({mu: 0, sigma: 1}).score(1)) )
print("Score of val = 2 for N(0,1): " + Math.exp(Gaussian({mu: 0, sigma: 1}).score(2)) )
print("Score of val = 3 for N(0,1): " + Math.exp(Gaussian({mu: 0, sigma: 1}).score(3)) )

Goal: Infer the mean and the variance of the Gaussian process that likely generated the data

var dataset = [10, 7, 9, 10, 15, 3];

var main = function () {

var mu = gaussian(7, 1)
var sigma = uniform(0,3)

var Xdist = Gaussian({mu: mu, sigma: sigma})
map(
function(val) { observe(Xdist, val);
// factor(x.score(val));
},
dataset
);

return [mu,sigma]
}

var dist = Infer(
{method: 'MCMC', samples: 10000},
main);

viz.auto(dist);
viz.marginals(dist);

Some more statistics on the mean:

///fold:
var dataset = [10, 7, 9, 10, 15, 3];

var main = function () {

var mu = gaussian(7, 1)
var sigma = uniform(0,3)

var Xdist = Gaussian({mu: mu, sigma: sigma})
map(
function(val) { observe(Xdist, val)
// factor(Xdist.score(val));
},
dataset
);

return [mu,sigma]
}

var d = Infer(
{method: 'MCMC', samples: 10000},
main);

viz.auto(d);
///

var getMean = function(dist_){
var support = dist_.support()
var e = map(function(x){return x * Math.exp(dist_.score(x))},support)
return sum(e)
}

var getSecondMoment = function(dist_){
var support = dist_.support()
var e = map(function(x){return (x * x) * Math.exp(dist_.score(x))},support)
return sum(e)
}

var getVar = function(dist_){
var secondMoment = getSecondMoment(dist_)
var mean = getMean(dist_)
var mean_square = mean * mean
var Variance = (secondMoment - mean_square)
return Variance
}

var getNthMoment = function(dist_,n){
var support = dist_.support()
var e = map(function(x){return Math.pow(x,n) * Math.exp(dist_.score(x))},support)
return sum(e)
}

display("Mean:")
display(getMean(d))
display("Variance:")
display(getVar(d))

Students Learning (BCM Book)

Goal: The exam consists of 40 T/F questions. In total, 15 students are taking the test. Given the scores of the students, infer whether they were studying for the exam or just randomly guessing.

//some data:

var allscores = [21, 17, 21, 18, 22, 31, 31, 34, 34, 35, 35, 36, 39, 36, 35 ];

var main = function () {

var preparationprob = uniform(0.5, 1)

var guessprob = 0.5

var total = 40

var studied = map(
function(testscore) {

var z = bernoulli(0.5);
var theta = z? preparationprob : guessprob;

var k = Binomial({p: theta, n: total})

// condition (k == testscore)
factor(k.score(testscore))

return z
},
allscores);

return studied
}

var dist = Infer(
{method: 'MCMC', samples: 1000},
main);

viz.marginals(dist);

Return to Simple Rate Inference (BCM Book)

Goal: infer the underlying success rate for a binary process. We want to determine the posterior distribution of the rate theta.

Show how to distinguish between:

• Prior distribution over parameters -- describes our initial belief.
• Prior predictive distribution -- describes what data to expect from the model given the prior beliefs.
• Posterior distribution over parameters -- describes our belief about the parameter distribution updated after observing data.
• Posterior predictive distribution -- describes what data to expect from the model given the updated beliefs.

var main = function () {
var n = 10

// prior:
var theta = beta(1,1)

// posterior:
var k = binomial(theta, n)

condition (k == 5)

// prior predictive:
var thetaprior = beta(1,1)
var priorpredictive = binomial(thetaprior, n)

// posterior predictive:
var postpredictive = binomial(theta, n)

return [theta, priorpredictive, postpredictive]

}

var dist = Infer(
{method: 'MCMC', samples: 10000},
main);
viz.marginals(dist);

Seven Scientists (BCM Book)

Goal: Seven Scientists are measuring the same (inherently noisy) phenomenon. Some scientists are better at measurement than the others. Based on the values, infer which scientists are more likely to identify the true value.

//some data:

var measurements = [-27, 3.50, 8.19, 9., 9.6, 9.95, 10.1 ];

var main = function () {

var mu = gaussian(9, 1)

var sigmas = map(
function(datapt) {

var sigma = uniform(0,10);

var x = Gaussian({mu: mu, sigma: sigma});
factor(x.score(datapt));

return sigma
},
measurements);

return sigmas
}

var dist = Infer(
{method: 'MCMC', samples: 10000},
main);

viz.marginals(dist);

Linear Regression (WebPPL Examples)

//some data:
var xs = [0, 1, 2, 3];
var ys = [0, 1, 4, 6];

var model = function() {
var m = gaussian(0, 2);
var b = gaussian(0, 2);
var sigma = gamma(1, 1);

var f = function(x) {
return m * x + b;
};

map2(
function(x, y) {
factor(Gaussian({mu: f(x), sigma: sigma}).score(y));
},
xs,
ys);

return f(4);
}

viz.auto(Infer({method: 'MCMC', samples: 10000}, model));

HMM (PPAML 2016)

var trueObservations = [false, false, false];

var transition = function(s) {
return s ? flip(0.7) : flip(0.3)
}
var observe = function(s) {
return s ? flip(0.9) : flip(0.1)
}
var hmm = function(n) {
var prev = (n == 1) ? {states: [true], observations: []} : hmm(n - 1);
var newState = transition(prev.states[prev.states.length - 1]);
var newObs = observe(newState);
return {
states: prev.states.concat([newState]),
observations: prev.observations.concat([newObs])
};
}
var stateDist = Infer({method: 'enumerate'},
function() {
var r = hmm(3);
map2(function(o,d){condition(o===d)}, r.observations, trueObservations)
return r.states;
}
)

viz.table(stateDist)