Table of Contents

## What does Markov Chain Monte Carlo do?

Markov Chain Monte Carlo provides an alternate approach to random sampling a high-dimensional probability distribution where the next sample is dependent upon the current sample. Gibbs Sampling and the more general Metropolis-Hastings algorithm are the two most common approaches to Markov Chain Monte Carlo sampling.

## Is Markov Chain Monte Carlo Bayesian?

The motivation behind Markov Chain Monte Carlo methods is that they perform an intelligent search within a high dimensional space and thus Bayesian Models in high dimensions become tractable.

**Does MCMC always converge?**

I often hear people often say they’re using a burn-in period in MCMC to run a Markov chain until it converges. But Markov chains don’t converge, at least not the Markov chains that are useful in MCMC.

**Is Monte Carlo a Bayesian?**

Both analytical approximations, such as the Laplace approximation and variational methods, and Monte Carlo methods have recently been used widely for Bayesian machine learning problems. It is interesting to note that Monte Carlo itself is a purely frequentist procedure [O’Hagan, 1987; MacKay, 1999].

### Why do we need MCMC for Bayesian?

MCMC can be used in Bayesian inference in order to generate, directly from the “not normalised part” of the posterior, samples to work with instead of dealing with intractable computations.

### Is MCMC sampling important?

The Bayesian inference of the GARCH model is performed by the MCMC method implemented by the Metropolis-Hastings algorithm and the importance sampling method for artificial return data and stock return data.

**What is burn in MCMC?**

Burn-in is a colloquial term that describes the practice of throwing away some iterations at the beginning of an MCMC run. The One Long Run web page explains why we can limit the discussion to just one run.

**Is Monte Carlo frequentist or Bayesian?**

Monte Carlo procedures are useful tools for such cases, and that is why Monte Carlo has been extensively used in both, frequentist and Bayesian analysis.

## Why do we need Markov Chain Monte Carlo?

The goal of MCMC is to draw samples from some probability distribution without having to know its exact height at any point. The way MCMC achieves this is to “wander around” on that distribution in such a way that the amount of time spent in each location is proportional to the height of the distribution.

## How does Monte Carlo algorithms work?

Monte Carlo simulation performs risk analysis by building models of possible results by substituting a range of values—a probability distribution—for any factor that has inherent uncertainty. It then calculates results over and over, each time using a different set of random values from the probability functions.

**Is rejection sampling MCMC?**

We might be able to get somewhere toward focusing the question by noting that while all four are Monte Carlo methods, Important sampling and Rejection sampling are not MCMC (that’s not to say they couldn’t be used within MCMC).

**How does Gibbs sampling work?**

The Gibbs Sampling is a Monte Carlo Markov Chain method that iteratively draws an instance from the distribution of each variable, conditional on the current values of the other variables in order to estimate complex joint distributions. In contrast to the Metropolis-Hastings algorithm, we always accept the proposal.

### What is effective sample size in MCMC?

Effective Sample Size. The Effective Sample Size (ESS) in the context of MCMC, measures the information content, or effectiveness of a sample chain. For example, 1,000 samples with an ESS of 200 have a higher information content than 2,000 samples with an ESS of 100.

### What is a trace plot?

Trace plots are similar to perturbation plots for non-mixture designs. They are used compare the effects of all the components in the design space. The factors tool is used to set the reference blend through which the traces are plotted.

**What is the burn in period?**

The preliminary steps, during which the chain moves from its unrepresentative initial value to the modal region of the posterior, is called the burn-in period. For realistic applications, it is routine to apply a burn-in period of several hundred to several thousand steps.

**How is MCMC used for Bayesian inference?**