Bayesian Statistics and Computing

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# Bayesian Statistics and Computing

## Lecture 12: Introduction to Bayesian Computing

#### *Yanfei Kang | BSC | Beihang University*

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# Strategies to summarize posterior

1. If we have familiar distributions, we can simply **simulate** samples in R.

2. **Grid approximation**.
    - Compute values of the posterior on a grid of points.
    - Approximate the continuous posterior by a discrete posterior.
    - Applicable for one- and two-parameter problems.
    
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# Bayesian computing

- Bear in mind that given the prior and data, the posterior is fixed and a Bayesian analysis really boils down to **summarizing the posterior**.

- Summarizing a `$p$`-dimensional posterior distribution is
challenging for large `$p$`.

- In 80's, Bayesian computing was unable to do this for
more than a few parameters.

- In the 90’s, new algorithms were developed that
revolutionized Bayesian statistics.

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# Approaches to Bayesian Computing

1. Just use a point estimate: Maximum a Posteriori (MAP) estimate.

2. Approximate the posterior as Gaussian.

3. Numerical integration.

4. Markov Chain Monte Carlo (MCMC) sampling.

**We will focus on 1-3 for this lecture.**

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# MAP

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# MAP

- Approximate based on posterior modes.

- Maximize the posterior
`$$\hat{\boldsymbol{\theta}}=\underset{\boldsymbol{\theta}}{\operatorname{argmax}} p(\boldsymbol{\theta} | \mathbf{Y})=\underset{\boldsymbol{\theta}}{\operatorname{argmax}} \log [f(\mathbf{Y} | \boldsymbol{\theta})]+\log [p(\boldsymbol{\theta})].$$`

- This is similar to the maximum likelihood estimation but
includes the prior.

- And you already know optimization.

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# Example: MAP and MLE

In 2018-19 season, Liverpool FC won 30 matches out of 38 matches in Premier league. Having this data, we’d like to make a guess at the probability that Liverpool FC wins a match in the next season.

- MLE?

- What if we know that Liverpool’s winning percentages for the past few seasons were around 50%?

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# MLE

- Assume Liverpool has a single winning probability `$\theta$`.

- `$p(k \mathrm{~wins~out~of~} n | \theta) = C_n^k\theta^k(1-\theta)^{n-k}.$`

- You can write `$p(\mathrm{Data}|\theta)$`.

- MLE?

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# MAP

- MLE is powerful when you have enough data.

- What if Liverpool only had 2 matches and they won the 2 matches?

- MAP includes the prior knowledge.

- Prior: `$p(\theta)\sim \operatorname{Beta}(10, 10)$`.

- Still, if the team wins 30 matches out of 38 matches in the past season, can you work out the MAP estimate of `$\theta$`?

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# Lab 9

Say `$Y|\theta \sim \text{Poisson}(N\theta),$` and `$\theta \sim \text{Gamma}(a, b)$`, find `$\hat{\theta}_{MAP}$`.

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# Approximate the posterior as Gaussian

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# Bayesian Central Limit Theorem

- We can actually approximate the posterior as Gaussian.

- Replies on **Berstein-Von Mises Theorem**: when the sample size is very large, the posterior does not depend on the prior.

- **Bayesian Central Limit Theorem (Bayesian CLT)**: when the sample size is very large, `$\theta|Y \sim \text{Normal}$`.

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# Bayesian CLT

- For large `$n$`, `$$\boldsymbol{\theta} \sim \operatorname{Normal}\left[\hat{\boldsymbol{\theta}}_{M A P}, \mathbf{I}\left(\hat{\boldsymbol{\theta}}_{M A P}\right)^{-1}\right].$$`

- The `$(j, k)$`-th element of `$\mathbf{I}$` is `$-\frac{\partial^{2}}{\partial \theta_{j} \partial \theta_{k}} \log [p(\boldsymbol{\theta} | \mathbf{Y})],$` evaluated at `$\hat{\theta}_{MAP}$`.

- Then we can do everything we do for normal distribution, e.g., sd, intervals etc.

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# Exercise

Say `$Y | \theta \sim \text { Binomial }(n, \theta)$`, and `$\theta \sim \operatorname{Beta}(0.5,0.5)$`, find the
Gaussian approximation for `$p(\theta|Y)$`.

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# Exercise continued

- The exact posterior is `$\theta | Y \sim \operatorname{Beta}(Y+1 / 2, n - Y +1 / 2)$`.

- MAP estimator `$\hat{\theta}=A /(A+B),$` where `$A = Y - 1/2$` and `$B = n - Y - 1/2$`.

- The posterior variance is approximated as `$\frac{1}{\frac{A}{\hat{\theta}^2}+\frac{B}{(1-\hat{\theta})^2}}$`.

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# Approximation in a small sample case

```r
theta <- seq(0.001,0.999,.001) # Grid of thetas for plotting
Y <- 2; n <- 5
# Compute the posterior mean and Fisher information matrix
A <- Y-0.5; B <- n-Y-0.5
theta_MAP <- A/(A+B)
Info  <- A/theta_MAP^2+B/(1-theta_MAP)^2
# Plot the true and approximate posteriors
post1 <- dbinom(Y,n,theta)*dbeta(theta,0.5,0.5)
post1 <- post1/sum(post1)
post2 <- dnorm(theta,theta_MAP,sqrt(1/Info))
post2 <- post2/sum(post2)
plot(theta,post1,type="l",lwd=2,
     xlab=expression(theta),ylab="Posterior")
abline(v=theta_MAP,col=3,lwd=2)
lines(theta,post2,col=2,lwd=2)
legend("topright",c("Exact","CLT","MAP"),bty="n",col=1:3,cex=1.5,lwd=2)
```
]

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<img src="figure/unnamed-chunk-1-1.svg" width="504" style="display: block; margin: auto;" />
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# Approximation in a medium sample case

```r
theta <- seq(0.001,0.999,.001) # Grid of thetas for plotting
Y <- 3; n <- 10
# Compute the posterior mean and Fisher information matrix
A <- Y-0.5; B  <- n-Y-0.5
theta_MAP <- A/(A+B)
Info  <- A/theta_MAP^2+B/(1-theta_MAP)^2
# Plot the true and approximate posteriors
post1 <- dbinom(Y,n,theta)*dbeta(theta,0.5,0.5)
post1 <- post1/sum(post1)
post2 <- dnorm(theta,theta_MAP,sqrt(1/Info))
post2 <- post2/sum(post2)
plot(theta,post1,type="l",lwd=2,
     xlab=expression(theta),ylab="Posterior")
abline(v=theta_MAP,col=3,lwd=2)
lines(theta,post2,col=2,lwd=2)
legend("topright",c("Exact","CLT","MAP"),bty="n",col=1:3,cex=1.5,lwd=2)
```
]

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<img src="figure/unnamed-chunk-2-1.svg" width="504" style="display: block; margin: auto;" />
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# Approximation in a large sample case

```r
theta <- seq(0.001,0.999,.001) # Grid of thetas for plotting
Y <- 30; n  <- 100
# Compute the posterior mean and Fisher information matrix
A  <- Y-0.5; B  <- n-Y-0.5
theta_MAP <- A/(A+B)
Info <- A/theta_MAP^2+B/(1-theta_MAP)^2
# Plot the true and approximate posteriors
post1 <- dbinom(Y,n,theta)*dbeta(theta,0.5,0.5)
post1 <- post1/sum(post1)
post2 <- dnorm(theta,theta_MAP,sqrt(1/Info))
post2 <- post2/sum(post2)
plot(theta,post1,type="l",lwd=2,
     xlab=expression(theta),ylab="Posterior")
abline(v=theta_MAP,col=3,lwd=2)
lines(theta,post2,col=2,lwd=2)
legend("topright",c("Exact","CLT","MAP"),bty="n",col=1:3,cex=1.5,lwd=2)
```
]

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<img src="figure/unnamed-chunk-3-1.svg" width="504" style="display: block; margin: auto;" />
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# Numerical integration

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# Numerical integration

- Many posterior summaries of interest are integrals over the
posterior.

- We usually can't solve integrals analytically.

- A grid approximation is a crude approach.

- Gaussian quadrature is better.

- The Iteratively Nested Laplace Approximation (INLA) is an
even more sophisticated method.

- Does not work well for large `$p$`.

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# Summary

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# Summary

- Bayesian inference requires summarizing the posterior distribution.

- When there are more than a few parameters, this requires advanced computational tools.

- Some methods that are deterministic.
    - MAP
    - Bayesian CLT
    - Numerical Integration
    
- Monte Carlo methods are more general (next lecture).