Model mis-specification is the norm. What happen to calibration in that case?

Misspecified models and calibration

What to do if your model is misspecified?

One obvious possibility is to make the model better. But in the following we show that under certain conditions, another possibility is to collect more data.

Now suppose we are using the “wrong prior”, i.e. data generation uses uniform prior but we base or posterior computation on a different, non-uniform prior.

Similarly to the last page, let’s do it for small (10 launches), medium (100), and large datasets (1000), plotting the nominal coverage (dashed) against the actual coverage (solid line)

Code

suppressPackageStartupMessages(require("ggplot2"))suppressPackageStartupMessages(require("dplyr"))suppressPackageStartupMessages(require("tidyr"))theme_set(theme_bw())# Using now the same non-uniform prior as before for posterior calculationK <-1000rdunif <-function(max) { return(ceiling(max*runif(1))) }posterior_distribution <-function(prior_probs, n_successes, n_trials){ K <-length(prior_probs)-1# K+1 values that your p can assume n_fails <- n_trials - n_successes p <-seq(0, 1, 1/K) posterior_probs <-# 1. this computes gamma(i) prior_probs *# - prior p^n_successes * (1-p)^n_fails # - likelihood posterior_probs <- posterior_probs/sum(posterior_probs) # 2. normalize gamma(i) post_prob <-rbind(p, posterior_probs)return(post_prob)}high_density_intervals <-function(alpha, posterior_probs){ ordered_probs = posterior_probs[,order(posterior_probs[2,], decreasing =TRUE)] cumulative_probs =cumsum(ordered_probs[2,]) index =which.max(cumulative_probs >= (1-alpha))return(ordered_probs[,1:index, drop=FALSE])}hdi_coverage_pr <-function(n_datapoints) { n_inclusions <-0for (repetition inseq(1:n_repeats)) { i <-rdunif(K +1) -1# Always generate the data using a uniform prior true_p <- i/K x <-rbinom(1, n_datapoints, true_p) post <-posterior_distribution(prior_used_for_computing_posterior, x, n_datapoints)# This if is just a hacky way to check if true parameter is in the HDI credible intervalif (sum(abs(true_p -high_density_intervals(alpha, post)[1,]) <10e-10) ==1) { n_inclusions <- n_inclusions +1 } }return(n_inclusions/n_repeats) # Fraction of simulation where the true parameter was in interval}prior_used_for_computing_posterior <-dnorm(seq(0, 1, 1/K),mean =0.2, sd=0.2)prior_used_for_computing_posterior <- prior_used_for_computing_posterior /sum(prior_used_for_computing_posterior)set.seed(1)n_repeats <-1000alpha <-0.1df <-data.frame("n_observations"=c(10, 100, 1000))df$coverage_pr <-sapply(df$n_observations, hdi_coverage_pr)ggplot(data=df, aes(x=n_observations, y=coverage_pr)) +ylim(0, 1) +xlab("Number of observations") +ylab("Actual coverage") +geom_hline(yintercept=1-alpha, linetype="dashed", color ="black") +geom_line()

Bad news: for small datasets we are no longer calibrated, in the worst possible way

Higher than dash line: would have meant inference is being conservative, i.e. more right than it actually claimed. That’s not too bad.

Lower than dash line: we are being overconfident or anti-conservative, which in some case can be reckless

Good news: this gets quickly corrected as dataset gets larger. Why?

Asymptotic calibration under certain misspecification

There is no general calibration theory for misspecified models, only special cases (outside of that, use simulation studies or cross-validation techniques)

Setup in which we have a theorem: when the data points \(y_i\) are iid given the parameters \(x\).

Bernstein-von Mises: under certain conditions, even when the prior is misspecified, the actual coverage of credible intervals converges to the nominal coverage.

Conditions include, but are not limited to:

\(x\) needs to live in a continuous rather than discrete space! I.e.: \(x \subset \mathbb{R}^d\)

Intuition: Bernstein-von Mises actually proves something stronger: convergence of the rescaled, centered posterior to a normal distribution, which is a continuous distribution (can you guess the scaling?)

“Bayesian Central Limit Theorem”

\(p(x)\) (the prior) is a density,

we assume it is non-zero in a neighborhood of the true parameter

we assume the posterior mean is consistent

recall: that means the error goes to zero as number of data points increases (something you will explore in this week’s exercise)

we assume the model is parametric:

\(x\) is allowed to be a vector, but you are not allowed to make it bigger as the number of observations increases.

Practical consequence:

for large datasets, and under certain condition, the prior is “washed away” by the likelihood.

The conditions are not crazy and hold in some situations of interest.

But there are also simple practical situations where the conditions do not hold (e.g. prior assigning zero mass to important regions).