Archive for October, 2011

Three Sampling Models for Fire Calls

October 31, 2011 Leave a comment

Continuing our example, suppose our prior beliefs about the mean count of fire calls \theta is Gamma(280, 4).  (Essentially this says that our prior guess at \theta is 70 and the prior standard deviation is about 4.2.)  But we’re unsure about the sampling model — it could be (M1) exponential(\theta), (M2) Poisson(\theta), or (M3) normal with mean \theta and standard deviation 12.

To get some sense about the best sampling model, I’ve plotted a histogram of the fire call counts below.  I’ve overlaid fitted exponential, Poisson, and normal distributions where I estimate \theta by the sample mean.

I think it is pretty clear from the graph that the exponential model is a poor fit.  The fits of the Poisson and normal (sd = 12) models are similar, although I’d give a slight preference to the normal model.

For each model, I compute the logarithm of the predictive probability f(y).   (I wrote a short function defining the log posterior of \theta and then used the laplace function in the LearnBayes package to compute \log f(y).)

Here are the results:

Model     \log f(y)
Poisson              -148.0368
exponential        -194.3483
normal(12)        -144.3027

The exponential model is a clear loser and the Poisson and normal(12) models are close.  The Bayes factor in support of the normal(12) model is

BF_{NP} = \exp( -144.3037 + 148.0368 ) = 41.8



Modeling fire alarm counts

October 30, 2011 Leave a comment

Sorry for the long delay since my last post.  It seems that one needs to keep a regular posting pattern, say several days a week. to keep this blog active.

We’re now talking about comparing models by Bayes factors.  To motivate the discussion of plausible models, I found the following website that gives the number of fire calls for each month in Franklinville, NC for the last several years.

Suppose we observe the fire call counts y_1, ..., y_N for N consecutive months.  Here is a general model for these data.

  1. y_1, ..., y_N are independent f(y | \theta)
  2. \theta has a prior g(\theta)
Also, suppose we have some prior beliefs about the mean fire count E(y).  We believe that this mean is about 70 and the standard deviation of this guess is 10.
Given this general model structure, we have to think of possible choices for f, the sampling density.  We think of the popular distributions, say Poisson, normal, exponential, etc.  Also we should think about different choices for the prior density.   For the prior, there are many possible choices — we typically choose one that can represent my prior information.
Once we decide on several plausible choices of sampling density and prior, then we’ll compare the models by Bayes factors.  To do this, we compute the prior predictive density of the actual data for each possible model.  It is very convenient to perform this calculation for discrete models (where the parameter \theta is discrete) and we’ll first illustrate Bayes factor computations in this special case

Cell phone story — part 3

October 5, 2011 Leave a comment

After talking about this problem in class on Monday, I realized that we don’t understand the texting patterns of undergraduates very well.  So I’m reluctant to specify an informative prior in this situation for the simple reason that I don’t understand this that well.  So I’m going to illustrate using bugs to fit this model using a vague prior.

I have observed the number of text messages for my son for 16 days in this months billing period.  Since his monthly allocation is 5000 messages, I am focusing on the event “number of messages in the 30 days exceeds 5000.”  Currently my son has had 3314 messages and so I’m interested in computing the predictive probability that the number of messages in the remaining 14 days exceeds 1686.

Here I’ll outline using the openbugs software with the R interface in the BRugs package to fit our model.

I’m assuming that you’re on a Windows machine and have already installed the BRugs package in R.

First we write a script that describes the normal sampling model.  Following Kruschke’s Doing Bayesian Data Analysis text, I can enter this model into the R console and write it to a file model.txt.

modelString = ”
model {
for( i in 1:n ){
y[i] ~ dnorm( mu, P )
mu ~ dnorm( mu0, P0 )
mu0 <- 100
P0 <- 0.00001
P ~ dgamma(a, b)
a <- 0.001
b <- 0.001

writeLines(modelString, con=”model.txt”)

# We load in the BRugs package (this includes the openbugs software).


# Set up the initial values for \mu and P in the MCMC iteration — this puts the values in a file called inits.txt.

bugsInits(list(c(mu = 200, P = 0.05)),
numChains = 1, fileName = “inits.txt”)

# Have openbugs check that the model specification is okay.


# Here is our data — n is the number of observations and y is the vector of text message counts.

dataList = list(
n = 16,
y=c(207, 121, 144, 229, 113, 262, 169, 330,
168, 132, 224, 188, 231, 207, 268, 321)

# Enter the data into openbugs.

modelData( bugsData( dataList ))

# Compile the model.


# Read in the initial values for the simulation.


# We are going to monitor the mean and the precision.

samplesSet( c(“mu”, “P”))

# We’ll try 10,000 iterations of MCMC.

chainLength = 10000

# Update (actually run the MCMC) — it is very quick.

modelUpdate( chainLength )

# The function samplesSample collects the simulated draws, samplesStats computes summary statistics.

muSample = samplesSample( “mu”)
muSummary = samplesStats( “mu”)

PSample = samplesSample( “P”)
PSummary = samplesStats( “P”)

# From this output, we can compute summaries of any function of the parameters of interest (like the normal standard deviation) and compute the predictive probability of interest.

Let’s focus on the prediction problem.  The variable of interest is z, the number of text messages in the 14 remaining days in the month.  The sampling model assumes that the number of daily text messages is N(\mu, \sigma), so the sum of text messages for 14 games is N(14 \mu, \sqrt{14} \sigma).

To simulate a single value of z from the posterior predictive distribution, we (1) simulate a value of (\mu, \sigma) from its posterior distribution, and (2) simulate z from a normal distribution using these simulated draws as parameters.

Categories: MCMC

Cell phone story — part 2

October 3, 2011 Leave a comment

It is relatively easy to set up a Gibbs sampling algorithm for the normal sampling problem when independent priors (of the conjugate type) are assigned to the mean and precision.  Here we outline how to do this on R.

We start with an expression for the joint posterior of the mean \mu and the precision P:

(Here S is the sum of squares of the observations about the mean.)

1.  To start, we recognize the two conditional distributions.

  • The posterior of \mu given P is given by the usual updating formula for a normal mean and a normal prior.  (Essentially this formula says that the posterior precision is the sum of the prior and data precisions and the posterior mean is a weighted average of the prior mean and the sample mean where the weights are proportional to the corresponding precisions.
  • The posterior of P given \mu has a gamma form where the shape is given by a + n/2 and the scale is easy to pick up.

2.  Now we’re ready to use R.  I’ve written a short function that implements a single Gibbs sampling cycle.  To understand the code, here are the variables:

– ybar is the sample mean, S is the sum of squares about the mean, and n is the sample size
– the prior parameters are (mu0, tau) for the prior and (a, b) for the precision
– theta is the current value of (\mu, P)

The function performs the simulations from the distributions [\mu | P] and P | \mu] and returns a new value of (\mu, P)

mu = theta[1]; P = theta[2]
P1 = 1/tau0^2 + n*P
mu1 = (mu0/tau0^2 + ybar*n*P) / P1
tau1 = sqrt(1/P1)
mu = rnorm(1, mu1, tau1)

a1 = a + n/2
b1 = b + S/2 + n/2*(mu – ybar)^2
P = rgamma(1, a1, b1)
c(mu, P)

All there is left in the programming is some set up code (bring in the data and define the prior parameters), give a starting value, and collect the vectors of simulated draws in a matrix.

Categories: MCMC

Cell phone story

October 2, 2011 Leave a comment

I’m interested in learning about the pattern of text message use for my college son.  I pay the monthly cell phone bill and I want to be pretty sure that he won’t exceed his monthly allowance of 5000 messages.

We’ll put this problem in the context of a normal distribution inference problem.  Suppose y, the number of daily text messages (received and sent) is normal with mean \mu and standard deviation \sigma.  We’ll observe y_1, ..., y_{13}, the number of text messages in the first 13 days in the billing month.  I’m interested in the predictive probability that the total of the count of text message in the next 17 days exceeds 5000.

We’ll talk about this problem in three steps.

  1. First I talk about some prior beliefs about (\mu, \sigma) that we’ll model by independent conjugate priors.
  2.  I’ll discuss the use of Gibbs sampling to simulate from the posterior distribution.
  3. Last, we’ll use the output of the Gibbs sampler to get a prediction interval for the sum of text messages in the next 17 days.
Here we talk about prior beliefs.  To be honest, I didn’t think too long about my beliefs about my son’s text message usage, but here is what I have.
  1. First I assume that my prior beliefs about the mean \mu and standard deviation \sigma of the population of text messages are independent.  This seems reasonable, especially since it is easier to think about each parameter separately.
  2. I’ll use conjugate priors to model beliefs about each parameter.  I believe my son makes, on average, 40 messages per day but I could easily be off by 15.  So I let \mu \sim N(40, 15).
  3. It is harder to think about my beliefs about the standard deviation \sigma of the text message population.   After some thought, I decide that my prior mean and standard deviation of \sigma are 5 and 2, respectively.  We’ll see shortly that it is convenient to model the precision P = 1/\sigma^2 by a gamma(a, b) distribution.  It turns out that P \sim gamma(3, 60) is a reasonable match to my prior information about \sigma.
In the next blog posting, I’ll illustrate writing a R script to implement the Gibbs sampling.
Categories: MCMC