# How to build a Bayesian regression model of a response that is a Gaussian mixture

Context:
My response looks like a mixture model with two classes as you can see on the picture.

I have a couple of predictors that perform relatively well in a linear regression (Bayesian or not). In the Bayesian context I am using MCMC sampling with stan like this:

\begin{align} \beta \sim {\rm Student}(7, 0, 20)& \\ \alpha \sim \mathcal{N}(0, 1)& \\ \sigma \sim \mathcal{N}(0, 1)& \\ y|X \sim \mathcal{N}(X\beta + \alpha, \sigma)& \end{align}

where $X$ are my predictors.

Here is an excerpt of the code in stan:

library(rstanarm)
model.glm <- stan_glm(y~poly(x1,4)+I(x2-x3), data=data, subset=train_index,
chains=5)


As you can imagine, my posterior is going to look like a normal distribution, which is confirmed by this chart:

predict <- posterior_predict(model.glm,data[-train_index])
ppc_dens_overlay(data[-train_index]y,predict[1:300,])  Problem: I would like my posterior to show the mixture model. However, I am having some issue to model it as I am fairly new to Bayesian stats. Question: How do you model a mixture model with predictor in MCMC sampling? Progress so far: I thought that I could use a multinomial prior (it could be binomial for my case but if I can make it generic why not!) with two classes, but then I am not sure where to go from there. This is the start that I tried to model but got stuck. \begin{align} \mu \sim {\rm Multinomial}(\tau, \gamma)& \\ X_j \sim \mathcal{N}(\mu_i, \sigma\star)& \\ Y|X \sim \mathcal{N}(X\beta, \sigma)& \end{align} • @gung edited my question. Let me know if it needs further editing. – zipp Feb 3 '17 at 16:50 • This question now seems to me to be on topic here. I am retracting my close vote. – gung - Reinstate Monica Feb 3 '17 at 17:28 • @gung thumbs up for your edit. I wish I could upvote it. – zipp Feb 3 '17 at 18:03 • You are possibly looking for latent class regression, but what you are showing us is marginal distribution ofY$and regression estimates the conditional distribution of$Y|X$and it's a different thing. – Tim Feb 3 '17 at 19:46 • @Tim thank you for your help. I think the math might be wrong there. Really what I am looking for is: How to build a Bayesian regression model of a response that is a Gaussian mixture. LCR might be it. I honestly didn't know about it. I was hoping to do that in stan but if I get the math right, the rest should be easy. – zipp Feb 3 '17 at 20:43 ## 1 Answer # Likelihood For a mixture of two Gaussians, the likelihood can be written as: $$y_i \sim \pi N(y_i|\alpha_0 + x_i\beta, \sigma_0) + (1-\pi) N(y_i|\alpha_1 + x_i\beta, \sigma_1)$$ where$\pi \in [0, 1]$. This is fine, but having two components in the likelihood makes sampling more difficult. A trick when dealing with mixture models is to augment the model with indicator variables that indicate to which class an observation belongs. So, for example,$\delta_i=0$if the observation belongs to the first class, and$\delta_i=1$if the observation belongs to the second class. If$p(\delta_i=0)=\pi$, the likelihood could be written as $$y_i |\delta_i \sim \left[N(y_i|\alpha_0 + x_i\beta, \sigma_0)\right]^{1-\delta_i} \times \left[N(y_i|\alpha_1 + x_i\beta, \sigma_1)\right]^{\delta_i},$$ and marginalizing out$\delta_i$would lead to the recovery of the original likelihood. # Priors In the model below,$\sigma^2_0$and$\sigma^2_1$have reference priors. Normal priors aren't the best choice for$\sigma^2_0$and$\sigma^2_1because the normal distribution has support on the real line, but the scale parameters can only take on positive values. Priors: \begin{align*} \alpha_0 & \sim N(0, \tau_{\alpha_0}^2) \\ \alpha_1 & \sim N(0, \tau_{\alpha_1}^2) \\ \beta & \propto 1 \\ p(\sigma_0) & \propto \frac{1}{\sigma_0^2} \\ p(\sigma_1) & \propto \frac{1}{\sigma_1^2} \\ \pi & \sim Unif(0, 1) \qquad \text{i.e. } Beta(1, 1). \end{align*} # MCMC Sampling The joint distribution up to a proportionality constant is given by \begin{align*} p(\alpha_0, \alpha_1, \beta, \sigma_0^2, \sigma_1^2 | \cdot) \propto & \ \exp\left( \frac{-\alpha_0^2}{2\tau_{\alpha_0}^2} \right) \exp\left( \frac{-\alpha_1^2}{2\tau_{\alpha_1}^2} \right) \frac{1}{\sigma_0^2} \frac{1}{\sigma_1^2} \\ & \times \prod_{i=1}^n \left[ \frac{1}{\sqrt{\sigma_0^2}} \exp\left( \frac{-(y_i - (\alpha_0 + x_i\beta))^2}{2 \sigma_0^2} \right)\right]^{1-\delta_i} \left[ \frac{1}{\sqrt{\sigma_1^2}} \exp\left( \frac{-(y_i - (\alpha_1 + x_i\beta))^2}{2 \sigma_1^2} \right)\right]^{\delta_i} \end{align*} After some algebra it's possible to find the conditional distributions of the parameters. In this case, all the full conditionals have closed forms, so a Gibbs sampler can be used to get draws from the joint posterior. ### Full conditionals \begin{align*} \sigma_0^2 | \cdot &\sim IG \left( \frac{n_0}{2}, \frac{1}{2} \sum_{i|\delta_i=0} \left( y_i - (\alpha_0 + x_i\beta) \right)^2 \right) \\ \sigma_1^2 | \cdot &\sim IG \left( \frac{n_1}{2}, \frac{1}{2} \sum_{i|\delta_i=1} \left( y_i - (\alpha_1 + x_i\beta) \right)^2 \right) \\ \end{align*} wherei|\delta_i=0$is used to denote the set of$i$such that$\delta_i=0$, and$n_0$is the count of the$\delta_i$where$\delta_i=0$. The same type of notation is used for$i|\delta_i=1$and$n_1$. Conditional on the$\delta_i$, the posterior distribution for$\betais \begin{align*} \beta | \cdot & \sim N(m, s^2) \\ \text{with} & \\ m & =\left( \sum_{i|\delta_i=0} x_i^2 \sigma_1^2 + \sum_{i|\delta_i=1} x_i^2 \sigma_0^2\right)^{-1} \left( \sigma_1^2 \sum_{i|\delta_i=0}(y_i x_i - \alpha_0 x_i) + \sigma_0^2 \sum_{i|\delta_i=1}(y_i x_i - \alpha_1 x_i) \right) \\ s^2 & = \frac{\sigma_0^2 \sigma_1^2}{\sum_{i|\delta_i=0} x_i^2 \sigma_1^2 + \sum_{i|\delta_i=1} x_i^2 \sigma_0^2} \end{align*} The conditional distributions for\alpha_0$and$\alpha_1are also normal \begin{align*} \alpha_0 & \sim N\left((\sigma_0^2 + n_0 \tau_0^2)^{-1} \tau_0^2 \sum_{i|\delta_i=0}(y_i - x_i \beta), \, \frac{\tau_0^2 \sigma_0^2}{\sigma_0^2 + n_0 \tau_0^2} \right) \\ \alpha_1 & \sim N\left((\sigma_1^2 + n_1 \tau_1^2)^{-1} \tau_1^2 \sum_{i|\delta_i=1}(y_i - x_i \beta), \, \frac{\tau_1^2 \sigma_1^2}{\sigma_1^2 + n_1 \tau_1^2} \right). \end{align*} The indicator variables for the class membership also need to be updated. These are Bernoulli with probabilities proportional to \begin{align*} p(\delta_i=0|\cdot) & \propto N(y_i|\alpha_0 + x_i \beta, \, \sigma_0^2) \\ p(\delta_i=1|\cdot) & \propto N(y_i|\alpha_1 + x_i \beta, \, \sigma_1^2). \\ \end{align*} # Results The MCMC predictions are bimodal as intended Here's the inference on the posterior distributions of the parameters, with the true values shown by the vertical red lines # A couple comments I suspect you know this, but I wanted to emphasize that the model I've shown here only has a single regression coefficient\beta$for both classes. It might not be reasonable to assume that both populations respond to the covariate in the same way. There are no restrictions on$\alpha_0$and$\alpha_1$in the prior specification, so in many cases there will be identifiability issues which lead to label switching. As the MCMC runs,$\alpha_0$may sometimes be larger than$\alpha_1$, and other times$\alpha_1$may be larger than$\alpha_0$. The changing values of$\alpha$will affect the$\delta_i$, causing them to swap labels from 0 to 1 and vice versa. These identifiability issues aren't a problem as long as your interest is only in the posterior predictive or inference on$\beta$. Otherwise changes may need to made in the prior, for example by forcing$\alpha_0 \leq \alpha_1$. I hope this is helpful. I included the code I used. I believe this can be done in Stan easily as well, but I haven't used Stan in a while so I'm not sure. If I have time later I may look into it. # Edit: Results using Stan I added some code for a similar model using Stan in case that is useful. Here's the same plot using the Stan model: set.seed(101) library(rstan) # Simulation truth -------------------------------------------------------- beta.tr <- 1.5 alpha.0.tr <- 2.0 alpha.1.tr <- -3.0 sigma.2.0.tr <- 0.5 sigma.2.1.tr <- 0.1 n.obs <- 200 class.proportion <- 0.3 # 30% in one component, 70% in the other delta.vec.tr <- rbinom(n.obs, size=1, prob=class.proportion) y.obs <- vector(length=n.obs) x.obs <- runif(n.obs, -1, 1) for(i in 1:n.obs) { if(delta.vec.tr[i]==0) { y.obs[i] <- rnorm(1, alpha.0.tr + x.obs[i]*beta.tr, sqrt(sigma.2.0.tr)) } else { y.obs[i] <- rnorm(1, alpha.1.tr + x.obs[i]*beta.tr, sqrt(sigma.2.1.tr)) } } # Priors ------------------------------------------------------------------ tau.2.alpha0 <- 30 tau.2.alpha1 <- 30 # Samplers ---------------------------------------------------------------- x.obs.sqrd <- x.obs^2 y.times.x <- y.obs*x.obs fn.sample.beta <- function(alpha.0, alpha.1, sigma.2.0, sigma.2.1, delta.vec) { sd.2.denom <- sum(delta.vec*x.obs.sqrd*sigma.2.1 + delta.vec*x.obs.sqrd*sigma.2.0) sd.2.num <- sigma.2.0*sigma.2.1 sd.2 <- sd.2.num/sd.2.denom mu <- (1/sd.2.denom)*sum(sigma.2.1*delta.vec*(y.times.x - alpha.0*x.obs) + sigma.2.0*delta.vec*(y.times.x - alpha.1*x.obs)) return(rnorm(1, mu, sqrt(sd.2))) } fn.sample.alpha <- function(beta, sigma.2, delta.vec, tau.2, class.idx) { n.members <- sum(delta.vec==class.idx) mu <- 1/(sigma.2+n.members*tau.2)*tau.2*sum((delta.vec==class.idx)*(y.obs - x.obs*beta)) sd.2 <- (tau.2*sigma.2)/(sigma.2 + n.members*tau.2) return(rnorm(1, mu, sqrt(sd.2))) } fn.sample.sigma <- function(beta, alpha, delta.vec, class.idx) { n.members <- sum(delta.vec==class.idx) shape <- n.members/2 rate <- (1/2)*sum((delta.vec==class.idx)*(y.obs - (alpha + x.obs*beta))^2) return(1/rgamma(1, shape, rate)) # Inverse-gamma } fn.sample.delta <- function(beta, alpha.0, alpha.1, sigma.2.0, sigma.2.1) { d0 <- dnorm(y.obs, alpha.0 + x.obs*beta, sqrt(sigma.2.0)) d1 <- dnorm(y.obs, alpha.1 + x.obs*beta, sqrt(sigma.2.1)) prob.1 <- d1/(d0 + d1) return(rbinom(n.obs, size=1, prob=prob.1)) } # MCMC -------------------------------------------------------------------- n.samples <- 20000 posterior.draws <- matrix(nrow=n.samples, ncol=5) colnames(posterior.draws) <- c("beta", "alpha.0", "alpha.1", "sigma.2.0", "sigma.2.1") delta.draws <- matrix(nrow=n.samples, ncol=n.obs) y.rep <- matrix(nrow=n.samples, ncol=n.obs) pi.draws <- vector(length=n.samples) # Initialization alpha.0 <- 0 alpha.1 <- 0 sigma.2.0 <- 1 sigma.2.1 <- 1 delta.vec <- as.numeric(y.obs < mean(y.obs)) for(b in 1:n.samples) { beta <- fn.sample.beta(alpha.0, alpha.1, sigma.2.0, sigma.2.1, delta.vec) alpha.0 <- fn.sample.alpha(beta, sigma.2.0, delta.vec, tau.2.alpha0, class.idx=0) alpha.1 <- fn.sample.alpha(beta, sigma.2.1, delta.vec, tau.2.alpha1, class.idx=1) sigma.2.0 <- fn.sample.sigma(beta, alpha.0, delta.vec, class.idx=0) sigma.2.1 <- fn.sample.sigma(beta, alpha.1, delta.vec, class.idx=1) delta.vec <- fn.sample.delta(beta, alpha.0, alpha.1, sigma.2.0, sigma.2.1) delta.draws[b,] <- delta.vec posterior.draws[b,] <- c(beta, alpha.0, alpha.1, sigma.2.0, sigma.2.1) # Posterior predictive for(i in 1:n.obs) { pi.prob <- rbeta(1, 1 + sum(delta.vec==0), 1 + n.obs - sum(delta.vec==0)) pi.draws[b] <- pi.prob if(runif(1) < pi.prob) { y.rep[b, i] <- rnorm(1, alpha.0 + x.obs[i]*beta, sqrt(sigma.2.0)) } else { y.rep[b, i] <- rnorm(1, alpha.1 + x.obs[i]*beta, sqrt(sigma.2.1)) } } } n.params <- ncol(posterior.draws) png(file="params.png") par.orig <- par(mfrow=c(2, 3)) for(i in 1:n.params) { param.name <- colnames(posterior.draws)[i] plot(density(posterior.draws[,i]), main="", xlab=param.name) abline(v=get(paste(param.name, ".tr", sep="")), col="red") } par(par.orig) dev.off() png(file="postpreds.png") plot(density(y.obs), xlab="", col="red", ylim=c(0, 0.5), main="", type='n') for(b in 1:n.samples) { lines(density(y.rep[b,]), col=rgb(0, 0, 1, 0.1)) } lines(density(y.obs), xlab="", col="red", ylim=c(0, 0.5)) legend("topleft", c("y", "y.rep"), col=c("red", "blue"), lty=1, cex=0.8) dev.off() # Stan -------------------------------------------------------------------- model.code <- ' data { int<lower=1> K; // number of mixture components int<lower=1> N; // number of data points real y[N]; // observations real x[N]; // covariates } parameters { simplex[K] pi_prob; // mixing proportions real alpha[K]; // locations of mixture components real<lower=0> sigma[K]; // scales of mixture components real beta; // regression coefficient } model { real ps[K]; // temp for log component densities alpha ~ normal(0, 30); for (n in 1:N) { for (k in 1:K) { ps[k] = log(pi_prob[k]) + normal_lpdf(y[n] | alpha[k] + x[n] * beta, sigma[k]); } target += log_sum_exp(ps); } } generated quantities { int z; // class index real y_rep[N]; for (i in 1:N) { z = categorical_rng(pi_prob); y_rep[i] = normal_rng(alpha[z] + beta * x[i], sigma[z]); } }' model.dat <- list(x=x.obs, y=y.obs, N=length(x.obs), K=2) stan.fit <- stan(model_code=model.code, model_name="gaussian_mixture", data=model.dat, iter=5000, chains=4, thin=1, warmup=2000, seed=101) y.rep.stan <- extract(stan.fit)$y_rep
png(file="postpreds_stan.png")
plot(density(y.obs), xlab="", col="red", ylim=c(0, 0.5), main="", type='n')
for(b in 1:nrow(y.rep.stan)) {
lines(density(y.rep.stan[b,]), col=rgb(0, 0, 1, 0.1))
}
lines(density(y.obs), xlab="", col="red", ylim=c(0, 0.5))
legend("topleft", c("y", "y.rep.stan"), col=c("red", "blue"), lty=1, cex=0.8)
dev.off()

• awesome! Some quicks note: 1. why did you take π∼Unif(0,1)? 2. In your stan code, your sigma is uniform (since you did not specify it). However, in your explaination it was sigma∼N(0,τ2α0). Any reason? 3. Why no prior on Beta? 4. you can easily change your code to have more than one predictor by change X to a matrix[N,NbrPred], vector[NbrPred] beta and it should run. – zipp Feb 6 '17 at 0:28
• Ah, yes good catch. The Stan model and the other model are similar but not identical. I didn't really have any special reason for choosing a uniform prior for $\pi$. Mostly it just seemed convenient. Same sort of reason for the $\beta$ prior -- basically it was just because I didn't want to do the calculations. There is no reason you couldn't have one though. Thanks for the tip about Stan! I am still a Stan novice. – AtALoss Feb 6 '17 at 1:01
• I also advise you to look at library(bayesplot) that easily allow you to do nice plots (such as the one I made in the question). – zipp Feb 6 '17 at 1:15