The Beta distribution (and more generally the Dirichlet) are probably my favorite distributions. However, sometimes only limited information is available when trying set up the distribution. For example maybe you only know the lowest likely value, the highest likely value and the median, as a measure of center. That information is sufficient to construct a basic form of the distribution. The idea of this post is not to elaborate in detail on Bayesian priors and posteriors but to give a real working example of using a prior with limited knowledge about the distribution, adding some collected data and arriving at a posterior distribution along with a measure of its uncertainty.

The example below is a simple demonstration on how a prior distribution and current data can be combined and form a posterior distribution. Earlier this year I gave a presentation at a conference where I modified this simple version of my code to be substantially more complex and I used the Dirichlet distribution to make national predictions based on statewide and local samples. So this approach has some very useful applied statistical properties and can be modified to handle some very complex distributions.

**A Note on Posterior Intervals**

The posterior interval (also called a *credible interval or credible region*) provides a very intuitive way to describe the measure of uncertainty. Unlike a confidence interval (discussed in one of my previous posts), a *credible interval* does in fact provide the probability that a value exists within the interval. With this interval it is based on calculating the probability of different values given the data. The graph above shows the different values (identified as theta) as well as the simulated posterior interval limits (alpha=.05 in black and .01 in light gray). In other words the probability that theta is a member of the 95% credible interval is 0.95 (written as = 0.95).

**The Prior and Posterior Distribution: An Example**

The code to run the beta.select() function is found in the LearnBayes package. This is a great function because by providing two quantiles one can determine the shape parameters of the Beta distribution. This is useful to find the parameters (or a close approximation) of the prior distribution given only limited information. If additional quantiles are known then they can be incorporated to better determine the shape parameters of the Beta distribution.

library(LearnBayes) Q = data.frame( quantile=c( median=0.5, maximum=0.99999, minimum=0.00001), prior=c( median=0.85, maximum=0.95, minimum=0.60) ) optimalBeta = function(Q) { q1q = Q$quantile[1] q1p = Q$prior[1] q2q = Q$quantile[2] q2p = Q$prior[2] q3q = Q$quantile[3] q3p = Q$prior[3] # find the beta prior using quantile1 and quantile2 q.med = list(p=q1q, x=q1p) q.max = list(p=q2q, x=q2p) q.min = list(p=q3q, x=q3p) # prior parameters using median and max, and median and min prior.A = beta.select(q.med,q.max) prior.B = beta.select(q.med,q.min) prior.Aa = prior.A[1] prior.Ab = prior.A[2] prior.Ba = prior.B[1] prior.Bb = prior.B[2] ## find the best possible beta prior ## Set a start and stop point range to find the best parameters if (prior.Aa < prior.Ba) { start.a = prior.Aa stop.a = prior.Ba } else { start.a = prior.Ba stop.a = prior.Aa } if (prior.Ab < prior.Bb) { start.b = prior.Ab stop.b = prior.Bb } else { start.b = prior.Bb stop.b = prior.Ab } seq.a = seq(from=start.a, to=stop.a, length.out=1000) seq.b = seq(from=start.b, to=stop.b, length.out=1000) seq.grid = expand.grid(seq.a, seq.b) prior.C.q1 = qbeta(q1q, seq.grid[,1], seq.grid[,2]) prior.C.q2 = qbeta(q2q, seq.grid[,1], seq.grid[,2]) prior.C.q3 = qbeta(q3q, seq.grid[,1], seq.grid[,2]) ## Different distance measurements, manhattan, euclidean, or otherwise. ## It would be interesting to run a simulation to measure a variety of distance measurements. prior.C.delta = abs(prior.C.q1 - q1p) + abs(prior.C.q2 - q2p) + abs(prior.C.q3 - q3p) ## prior.C.delta = sqrt( (prior.C.q1 - q1p)^2 + (prior.C.q2 - q2p)^2 + (prior.C.q3 - q3p)^2 ) optimize.seq = cbind(seq.grid, prior.C.q1, prior.C.q2, prior.C.q3, prior.C.delta) ## Minimize the delta, if the min-delta is not unique then choose the first occurence best.a = optimize.seq[,1][ optimize.seq[,6]==min(optimize.seq[,6])][1] best.b = optimize.seq[,2][ optimize.seq[,6]==min(optimize.seq[,6])][1] return(list(a=best.a,b=best.b)) } prior.dist = optimalBeta(Q) ########################################################## ## Take a look at only the prior ########################################################## curve(dbeta(x,prior.dist$a,prior.dist$b)) # plot the prior abline(v=Q$prior[1]) ########################################################## ## Take a look at only the likelihood with given successes ########################################################## calcLikelihood = function(successes, total){ curve(dbinom(successes,total,x)) # plot the likelihood } calcLikelihood(45, 50) ## e.g. 45/50 sucesses ## calculate some properties of the Beta distribution calcBetaMode = function(aa, bb) { beta.mode = (aa - 1)/(aa + bb - 2) return(beta.mode) } calcBetaMean = function(aa, bb) { beta.mean = (aa)/(aa + bb) return(beta.mean) } calcBetaVar = function(aa, bb) { beta.var = (aa * bb)/(((aa + bb)^2) * (aa + bb + 1)) return(beta.var) } calcBetaMedian = function(aa, bb) { beta.med = (aa-1/3)/(aa+bb-2/3) return(beta.med) } calcBetaSkew = function(aa, bb) { beta.skew = ( 2*(bb-aa)*sqrt(aa+bb+1) ) /( (aa+bb+2)/sqrt(aa+bb) ) return(beta.skew) } ########################################################## ## Take a look at the prior, likelihood, and posterior ########################################################## priorToPosterior = function(successes, total, a, b) { ## Note the rule of succession likelihood.a = successes + 1 likelihood.b = total - successes + 1 ## Create posterior posterior.a = a + successes; posterior.b = b + total - successes theta = seq(0.005, 0.995, length = 500) ## Calc density prior = dbeta(theta, a, b) likelihood = dbeta(theta, likelihood.a, likelihood.b) posterior = dbeta(theta, posterior.a, posterior.b) ## Plot prior, likelihood, and posterior ## Can be used to scale down the graph if desired. ## However, the density is different for each prior, likelihood, posterior m.orig = apply( cbind(prior, likelihood, posterior), 2, max) m = max(c(prior, likelihood, posterior)) plot(theta, posterior, type = "l", ylab = "Density", lty = 2, lwd = 3, main = paste("Prior: beta(", round(a,2), ",", round(b,2), "); Data: B(", total, ",", successes, "); ", "Posterior: beta(", round(posterior.a,2), ",", round(posterior.b,2), ")", sep=""), ylim = c(0, m), col = 1) lines(theta, likelihood, lty = 1, lwd = 3, col = 2) lines(theta, prior, lty = 3, lwd = 3, col = 3) legend("topleft",y=m, c("Prior", "Likelihood", "Posterior"), lty = c(3, 1, 2), lwd = c(3, 3, 3), col = c(3, 2, 1)) prior.mode = calcBetaMode(a, b) likelihood.mode = calcBetaMode(likelihood.a, likelihood.b) posterior.mode = calcBetaMode(posterior.a, posterior.b) prior.mean = calcBetaMean(a, b) likelihood.mean = calcBetaMean(likelihood.a, likelihood.b) posterior.mean = calcBetaMean(posterior.a, posterior.b) prior.med = calcBetaMedian(a, b) likelihood.med = calcBetaMedian(likelihood.a, likelihood.b) posterior.med = calcBetaMedian(posterior.a, posterior.b) prior.var = calcBetaVar(a, b) likelihood.var = calcBetaVar(likelihood.a, likelihood.b) posterior.var = calcBetaVar(posterior.a, posterior.b) prior.skew = calcBetaSkew(a, b) likelihood.skew = calcBetaSkew(likelihood.a, likelihood.b) posterior.skew = calcBetaSkew(posterior.a, posterior.b) print(paste("Mode: prior=",prior.mode,"; Likelihood=",likelihood.mode,"; Posterior=",posterior.mode)) print(paste("Mean: prior=",prior.mean,"; Likelihood=",likelihood.mean,"; Posterior=",posterior.mean)) print(paste("~Approx Median (for a and b > 1): prior=",prior.med,"; Likelihood=",likelihood.med,", for Posterior=",posterior.med)) print(paste("Var: prior=",prior.var,"; Likelihood=", likelihood.var,"; Posterior=",posterior.var)) print(paste("Skewness: prior=",prior.skew,"; Likelihood=",likelihood.skew,"; Posterior=",posterior.skew)) return(list(a=posterior.a,b=posterior.b)) } posterior.out = priorToPosterior(25,50, prior.dist$a, prior.dist$b) # 25/50 is current data beta.sim = rbeta(1000000,posterior.out$a, posterior.out$b) abline(v=quantile(beta.sim, prob=c(.05/2, 1-.05/2)), col='#000000', lwd=2) abline(v=quantile(beta.sim, prob=c(.01/2, 1-.01/2)), col='#EEEEEE', lwd=2)

An R simulation of beta priors and posteriors using the LearnBayes package. http://t.co/KLjPiSod2T #bayesian #statistics #prior #posteriors

RT @statisticsblog: An R simulation of beta priors and posteriors using the LearnBayes package. http://t.co/KLjPiSod2T #bayesian #statistic…