Author: VIBASS

# Practical 4: Simulation-based Bayesian inference

#### VIBASS

# 1 Simulation-based inference in a Bayesian quadratic model

## 1.1 Introduction

We resume the analysis of the data set of the library from the previous practical session. We are again interested in explaining the fuel consumption of the cars (variable ) as a function of their power ().

We start by loading the data and redoing the frequentist fit from the previous session:

    library(ISLR)

    ##
    ## Attaching package: 'ISLR'

    ## The following object is masked _by_ '.GlobalEnv':
    ##
    ##     Auto

    library(LaplacesDemon)

    Auto$horse.std <- (Auto$horsepower-mean(Auto$horsepower))/sd(Auto$horsepower)
    # Quadratic fit
    Fit1 <- lm( mpg ~ poly(horse.std, degree = 2, raw = TRUE), data = Auto)
    summary(Fit1)

    ##
    ## Call:
    ## lm(formula = mpg ~ poly(horse.std, degree = 2, raw = TRUE), data = Auto)
    ##
    ## Residuals:
    ##      Min       1Q   Median       3Q      Max
    ## -14.7135  -2.5943  -0.0859   2.2868  15.8961
    ##
    ## Coefficients:
    ##                                          Estimate Std. Error t value Pr(>|t|)
    ## (Intercept)                               21.6274     0.2852   75.83   <2e-16
    ## poly(horse.std, degree = 2, raw = TRUE)1  -8.0478     0.2953  -27.25   <2e-16
    ## poly(horse.std, degree = 2, raw = TRUE)2   1.8231     0.1809   10.08   <2e-16
    ##
    ## (Intercept)                              ***
    ## poly(horse.std, degree = 2, raw = TRUE)1 ***
    ## poly(horse.std, degree = 2, raw = TRUE)2 ***
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ##
    ## Residual standard error: 4.374 on 389 degrees of freedom
    ## Multiple R-squared:  0.6876, Adjusted R-squared:  0.686
    ## F-statistic:   428 on 2 and 389 DF,  p-value: < 2.2e-16

    plot(mpg ~ horse.std, data = Auto)
    where <- seq(min(Auto$horse.std), max(Auto$horse.std), length = 100)
    lines(where, predict(Fit1, data.frame(horse.std=where)), col = 2, lwd = 2)

We will now assume that we have prior information on the parameters of this analysis. That prior information may be summarised in the following conjugate prior: \[\pi(\boldsymbol{\beta},\sigma^2)\sim N_3(\boldsymbol{\beta}\mid\mathbf{0}_3,5\sigma^2\mathbf{I}_3)IG(\sigma^2\mid 10,300)\]

## 1.2 Posterior distribution for the model parameters

As mentioned in the theoretical session, the posterior distribution of these parameters under this model is:

\[\pi(\boldsymbol{\beta}|\sigma^2,\boldsymbol{y})\sim N_{p+1}\left((\boldsymbol{S}^{-1}+\boldsymbol{X}'\boldsymbol{X})^{-1}(\boldsymbol{X}'\boldsymbol{y}+\boldsymbol{S}^{-1}\boldsymbol{m}),\sigma^2(\boldsymbol{S}^{-1}+\boldsymbol{X}'\boldsymbol{X})^{-1}\right)\] and \[\pi(\sigma^2|\boldsymbol{y})\sim IG\left(a+\frac{n}{2},b+\frac{s_e^2+(\boldsymbol{m}-\hat{\boldsymbol{\beta}})'(\boldsymbol{S}+(\boldsymbol{X}'\boldsymbol{X})^{-1})^{-1}(\boldsymbol{m}-\hat{\boldsymbol{\beta}})}{2}\right)\]

where \(m=\textbf{0}_3\), \(S=5\cdot\textbf{I}_3\), \(a=10\) and \(b=300\). Unlike Jeffreys’ prior, for this model no analytical expression can be deduced for the marginal posterior distribution \(\pi(\boldsymbol{\beta}\mid\mathcal{D})\), so the practical use of this model becomes quite problematic.

## 1.3 Analytical inference

We are going to generate functions for calculating the posterior distributions \(\pi(\sigma^2\mid \mathcal{D})\), \(\pi(\boldsymbol{\beta}\mid \sigma^2, \mathcal{D})\) and \(\pi(\boldsymbol{\beta}, \sigma^2 \mid \mathcal{D})\)

    X <- cbind(rep(1, dim(Auto)[1]), Auto$horse.std, Auto$horse.std^2)
    y <- matrix(Auto$mpg, ncol = 1)

    betahat <- matrix(Fit1$coefficients, ncol = 1)
    RSS <- sum(residuals(Fit1)^2)

    # P(sigma^2|y)
    Psigma2<-function(sigma2){
      dinvgamma(sigma2, 10+nrow(Auto)/2,
                300+(RSS+t(betahat)%*%solve(5*diag(3)+solve(t(X)%*%X))%*%betahat)/2)
    }

    # P(beta|sigma^2,y)
    PBetaGivenSigma2<-function(beta,sigma2){
      dmvn(beta, as.vector(solve(diag(3)/5+t(X)%*%X)%*%(t(X)%*%y)),
           sigma2*solve(diag(3)/5+t(X)%*%X))
    }

    # P(beta,sigma^2|y)
    JointPosterior<-function(beta,sigma2){
      P1 <- Psigma2(sigma2)
      P2 <- PBetaGivenSigma2(beta, sigma2)
      P1*P2
    }

We can use these functions to plot, for example, the posterior marginal distribution of \(\sigma^2\), \(\pi(\sigma^2\mid\mathcal{D})\):

    plot(seq(10, 30, by = 0.1), Psigma2(seq(10, 30, by=0.1)), type = "l",
         xlab = "sigma2", ylab = "density", main = "posterior marginal density of sigma2")

However, the marginal posterior distribution of \(\boldsymbol{\beta}\), \(\pi(\boldsymbol{\beta}\mid \mathcal{D})\) is not analytic. We know the conditional posterior distribution of \(\pi(\boldsymbol{\beta}|\sigma^2, \mathcal{D})\), i.e., we know that distribution for given values of \(\sigma^2\) but not for unknown values of this parameter. Possibly, simulation in this context could make things much easier.

## 1.4 Simulation-based inference

We could use the composition method in order to draw samples of \(\pi((\boldsymbol{\beta},\sigma^2)\mid \mathcal{D})\). This could be done as follows:

    # Sample from pi(sigma^2|y)
    sigma2.sample <- rinvgamma(10000, 10+nrow(Auto)/2,
                               300+(RSS+t(betahat)%*%(diag(3)+solve(t(X)%*%X))%*%betahat)/2)

    # Sample from pi(beta|y, sigma^2)
    beta.sample<-t(sapply(sigma2.sample,function(sigma2){
      rmvn(1, as.vector(solve(diag(3)+t(X)%*%X)%*%(t(X)%*%y)),
           sigma2*round(solve(diag(3)+t(X)%*%X),5))
    }))
    # Sample from pi(beta, sigma^2| y)
    posterior.sample <- cbind(beta.sample, sigma2.sample)

With this, we have 10000 draws of the joint posterior distribution of \(\pi((\boldsymbol{\beta},\sigma^2)\mid \mathcal{D})\) and, from that sample, we could evaluate what we found of interest from these parameters. For example:

    # Posterior mean of each parameter:
    apply(posterior.sample, 2, mean)

    ##                                           sigma2.sample
    ##     21.546169     -8.050761      1.844066     20.937977

    # Posterior standard deviation of each parameter:
    apply(posterior.sample, 2, sd)

    ##                                           sigma2.sample
    ##     0.2981074     0.3080190     0.1877347     1.4600682

    # Posterior probability of being higher than 0
    apply(posterior.sample>0, 2, mean)

    ##                                           sigma2.sample
    ##             1             0             1             1

Beyond the parameters of the model, we could take advantage of these samples to make inference about quantities of even greater interest such as, for example, the curve describing the relationship between the two variables. Thus, we can plot the posterior mean of the curve, and even assess its variability, by calculating the curve for each of the samples drawn from \(\pi((\boldsymbol{\beta},\sigma^2)\mid \textbf{y})\):

    plot(mpg ~ horse.std, data = Auto)

    # Posterior mean of the curve
    where <- seq(min(Auto$horse.std), max(Auto$horse.std), length = 100)
    posterior.mean.curve <- as.vector(cbind(rep(1,length(where)), where, where^2)%*%
                                        matrix(apply(posterior.sample[,1:3], 2, mean),ncol=1))
    lines(where, posterior.mean.curve, col = 2, lwd = 2)

    # 95% credible band for the curve
    posterior.curves <- t(cbind(rep(1,length(where)), where, where^2)
                         %*%t(posterior.sample[,1:3]))
    Band <- apply(posterior.curves, 2, function(x){quantile(x, c(0.025,0.975))})
    lines(where, Band[1,], col = 3, lwd = 1.5, lty = 2)
    lines(where, Band[2,], col = 3, lwd = 1.5, lty=2)

## 1.5 Predictive inference

For given values of \(\boldsymbol{\beta}\) and \(\sigma^2\) we would know the predictive distribution of for a given value of : \[\pi(y^*\mid \hat{\beta},\hat{\sigma}^2)=N(y^*|\textbf{x}^*\cdot\hat{\beta},\hat{\sigma}^2),\] where \(\mathbf{x}^*\) is a vector with the covariates of the individual of interest. In that case we could use the composition method to sample values of a new hypothetical \(y^*\) for any given value of and the sampled values \(\{(\beta_1,\sigma_1^2),\ldots,(\beta_n,\sigma_n^2)\}\).

    SampleY <- function(at){
      mu <- beta.sample%*%matrix(c(1, at, at^2), ncol = 1)
      rnorm(nrow(mu), mu, sqrt(sigma2.sample))
    }
    samples <- sapply(where, SampleY)

With this sample of the predictive distribution we can even plot prediction bands for the s:

    quantiles <- apply(samples, 2, quantile, c(0.025,0.975))
    plot(mpg ~ horse.std, data = Auto)
    lines(where, quantiles[1,], col = 4, lty = 3)
    lines(where, quantiles[2,], col = 4, lty = 3)

Analytical inference on this predictive distribution is far more complicated.

# 2 Time to individual work

We propose below an individual exercise that pursues to consolidate the basic concepts that we have learned in the previous theoretical session and that we have been practising in this session.

Exercise

We return to the analysis of the dataset by age and race of the children. Perform simulation-based inference on this data set assuming a non-informative Jeffreys’ prior for the parameters. Assess the probability that a 10-year-old child weighs more than 30 (kg.).