Bayesian Inference

Likelihood Based Inference with Incomplete Data (Nonignorable)

Specific methods are required to make inference under nonignorable nonresponse assumptions, that is when the value of the variable that is missing is related to some values which are not observed by the analyst (e.g. the missing values themselves)

Introduction to Bayesian Inference

Bayesian inference is a method of statistical inference in which Bayes theorem is used to update the probability for a hypothesis as more evidence or information becomes available

Likelihood Based Inference with Incomplete Data

When making inference with missing data, any statistical method must rely on either explicit or implicit assumptions about the mechanism which lead some of the values to be missing

Bayesian Iterative Simulation Methods

The most popular class of Bayesian iterative methods is called Markov chain Monte Carlo (MCMC), which comprises different algorithms for sampling from a probability distribution. The more steps that are included, the more closely the distribution of the sample matches the actual desired distribution