Random walk example, Part 2

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From the course by University of California, Santa Cruz

Bayesian Statistics: Techniques and Models

185 ratings

University of California, Santa Cruz

Bayesian Statistics: Techniques and Models

185 ratings

This is the second of a two-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, which introduces Bayesian methods through use of simple conjugate models. Real-world data often require more sophisticated models to reach realistic conclusions. This course aims to expand our “Bayesian toolbox” with more general models, and computational techniques to fit them. In particular, we will introduce Markov chain Monte Carlo (MCMC) methods, which allow sampling from posterior distributions that have no analytical solution. We will use the open-source, freely available software R (some experience is assumed, e.g., completing the previous course in R) and JAGS (no experience required). We will learn how to construct, fit, assess, and compare Bayesian statistical models to answer scientific questions involving continuous, binary, and count data. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. The lectures provide some of the basic mathematical development, explanations of the statistical modeling process, and a few basic modeling techniques commonly used by statisticians. Computer demonstrations provide concrete, practical walkthroughs. Completion of this course will give you access to a wide range of Bayesian analytical tools, customizable to your data.

From the lesson

Markov chain Monte Carlo (MCMC)

Metropolis-Hastings, Gibbs sampling, assessing convergence

Demonstration10:59

Random walk example, Part 112:59

Random walk example, Part 216:49

Meet the Instructors

Matthew Heiner
Doctoral Student
Statistics

Now we're ready to set up the problem.

I have copied here a vector of the data values and we've called it y.

So what we need is to calculate y bar which is the sample mean of y.

We also need to run y so that the values are saved in our R session.

And now we can run this line to calculate y bar, the mean of y.

We also need n, which is the length of y.

We'll run that and just to check it, it is 10.

And as part of our analysis let's just look at a histogram of these data points.

So we'll do a histogram of y, where again, we want the frequency to be false,

so that we're looking, instead of counts we're looking at probabilities.

And we want the x axis

to be between the values of negative 1 and

3 just so that we have enough view on both sides of the data.

Let's run this histogram.

Here's what the data look like.

Let's also add the individual data points.

We can do that with the points function.

Where we add the y vector.

And we want, on the y axis,

we want these to appear on 0 so that they appear along the x axis.

So we're going to repeat 0 n times for our y coordinant.

And we'll actually just run this.

So that adds the actual data points.

So we can see where they are.

We can also add y bar, the sample mean to see where that's located.

Again, we'll use points, and this time y bar and 0.

And to distinguish it from the data points,

let's use pch-19 that creates a field circle.

So when I run this, it adds y bar, the sample mean.

So we can see where the sample mean of our data are.

Let's add the plot of our prior distribution for

the mean to see how it compares with our data.

We can do with the curve function where we're going to use

the density of a t distribution dt for

generic x variable, which we haven't define is just the x axis in the plot.

And our t distribution has degree of freedom 1.

By default that already has location 0 and scale 1.

To distinguish it from the histogram,

let's give it a different line type, lty=2.

That will cause it to be a dashed line.

And then of course, add=true so that it adds this plot on top of the previous one.

Let's run this curve.

Here's our prior distribution for mu.

As you can see, there's a little bit of a discrepancy between our prior for

mu, and what the data says mu should be.

The prior has most of its probability mass in this region here, near 0.

However, the data suggest that the mean is up here near 1.

We expect that the posterior distribution from mu will

have a mean as a compromise somewhere between 0 and 1.

We're now ready to run the sampler that we've just coded up.

I've already entered the LG function into the R session but

we still need to load the Metropolis-Hastings function.

So I'm going to press Cmd+Return to enter that function into the console.

And now we have the data.

We have y bar and we have n.

So let's move down here and start sampling.

We'll collect our posterior samples and a variable that we'll call post.

Referring to posterior.

It would be the result of calling our Metropolis-Hastings function,

where we need to give it these six arguments.

n, which we've defined, y bar which is the sample mean of the data.

N_iter, which is the number of iterations.

We've decided we want to do 1,000 iterations of our sampler.

There's also a shorthand for this which we can do, 1e3.

That's 10 to the 3rd power.

Let's initialize mu at 0.

We'll use candidate generating standard deviation equal to 3.

Before we start sampling, let's set the random generator seed.

So that we can reproduce our results if we need to.

So first thing we'll run is set.seed.

And then we'll run our Metropolis-Hastings function

using the parameters that we gave it.

If we've saved some object, and we're curious what's inside of it,

we can use the str function, str function in R.

And put the object in there to find out what's in the object.

So the structure of post is a list.

Just like we had our function output.

It's a list containing 1,000 iterations of our new variable.

And it tells us our acceptance rate.

Which in our case, for this run, was about 12%.

To help us explore posterior distributions,

we're going to use the coda package in R.

So let's load the coda package.

This R session does not currently have coda installed.

So first we're going to need to install the package.

install.packages.

And we'll install Coda.

So we'll run that line.

When it installs a package,

it asks us which CRAN mirror we want to download the package from.

We're in California, so let's select California here.

Great, so hopefully now library coda will work for us.

Let's run this line, perfect.

It worked, now that we've loaded the coda library we can

use the trace plot function, which I'll describe in just a moment.

The trace plot function requires that we pass it an MCMC object.

Right now post is only a list.

So let's change post into an MCMC object by typing as.mcmc, that's also a function.

Post, specifically we want to look at the trace plot of mu.

So this is the mu vector in the post.

Our list of posts right here.

Let's run this line and see what happens.

Here's our trace plot.

So this is called a trace plot because it shows the history of our Markov chain.

And it gives us some basic feedback about whether the chain has reached

the stationary distribution.

It appears that our proposal step size in this case was a little bit

too large because we had an acceptance rate below 23%.

It was somewhere, it was more like 12% I think in our case.

Yeah, here it is.

It was 12%.

You can see that in the trace plot.

As the sampler was moving along, for example in these early iterations,

mu got stuck at a single value for quite a long time, it happened again over here.

So in other words,

our Markov chain is not moving around as much as we would like it to.

We want to increase the acceptance rate.

To change the acceptance rate,

we need to rerun the sampler with a different candidate standard deviation.

Usually, if we increase the variability in the distribution

that creates the candidates, that will decrease the acceptance rate.

We want to increase the acceptance rate, so we want a smaller standard deviation.

Let's reduce it to say, 0.05.

Let's make it really small and see what happens.

So if we run the Metropolis-Hastings function and reassign it to post,

it'll override our old samples, but that's okay.

We still want to initialize mu at 0.

And we'll still run it for 1,000 iterations.

Let's run this line of code.

And let's look at the structure of post again.

So we have 1,000 samples, but

this time the acceptance rate was 94, almost 95%.

If we look at the trace plot for this chain, we see the opposite behavior.

Notice that the step sizes, because the candidate generating function have

a small variance, the step sizes are small.

And so the chain wanders.

Here the step sizes are too small.

It's going to take the chain a long time to explore the posterior distribution.

We can see that because the acceptance rate for our random walk is too high.

The first candidate standard deviation rate we tried was 3,

we over corrected by going down to 0.05, so let's try something in between.

Let's try 0.9, that's a compromise from the two values.

Let's run this, and now look at our acceptance rate.

Okay, our acceptance rate is down to 0.38, that's probably good.

We usually are looking for an acceptance rate between 0.23 and

0.5 when we're working with a random walk Metropolis-Hastings algorithm.

So let's look at the trace plot for this.

I'll run the trace plot command.

This trace plot looks much better.

It's exploring the posterior distribution.

It gets stuck every once in a while but that's okay.

It looks pretty good.

Now let's see what happens if we change our initial

value to something way far away from the truth.

So let's try an initial value of mu=30 We run that.

And look at the acceptance, we still get 0.38, that's good.

And let's look at our trace plot.

We'll run this line.

Here we can see the effect of using a crazy initial value.

We started mu way up here at 30, and it took maybe

almost 100 iterations to find the stationary distribution.

It was wondering for quite a long time, and then once it hits the stationary

distribution, it bounced around inside the stationary distribution.

But it looks like after 100 iterations, we've succeeded.

So if we discard or throw away the first 100 or so values,

it looks like the rest of the samples do come from the stationary distribution,

which is the posterior distribution we wanted.

Now let's plot the posterior density against the prior

to see how the data updated our belief about mu.

The first thing we'll do is get rid of those first 200 iterations

while the chain was still exploring.

We're going to call this a new object in our list called post.

And it'll be mu_keep.

It'll be the iterations of mu that we want to keep.

It'll come from the mu object in our list for

post where we get rid of.

We're going to discard iterations 1 through,

let's just get rid of the first 100, okay.

So this says, we're going to index the mu vector

by taking away the first 100 elements.

Let's run that line, it saves.

So that we can look at the structure of post,

we now have mu.keep or mu_keep.

So let's plot a density estimate so

it's plot (density),

a density estimate of our posterior draws from mu, post$mu_keep.

Let's use the same limits to our x axis that we did before.

So that would be xlim going from negative 1 to 3.

And let's take a look at this plot.

Let's run this.

So this is a density estimate of the posterior distribution

based on the 10 data points.

Again, let's compare this to the data and the prior.

First, let's draw the prior.

Again, we can reuse our old code up here,

where we drew the prior for the density of the t distribution with

1 degree of freedom, where add=true so that we would write it over the same plot.

Let's run this line.

So here is our prior distribution for mu and

then based on the data,here is our posterior distribution for mu.

Let's also add the sample mean.

So this is where the data said mu should be.

This is where the prior said mu should be.

The posterior distribution does look like a compromise between

the prior distribution and the data.

This results we've obtained are encouraging but they are preliminary.

We still need to investigate a little more formally whether our Markov chain

has actually converged to the stationery distribution,

and we're going to explore this in a future lesson.

Creating posterior samples using a Metropolis-Hastings algorithm

can be time consuming and require a lot of fine tuning

like we did with our candidate standard deviation here.

The good news is that we can rely on software to do most of the work for us.

So in the next couple of videos,

we're going to introduce a program that will make posterior sampling easy.

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