Overview

... MCMC methods are a collection of techniques that use pseudo-random (computer simulated) values to estimate solutions to mathematical problems MCMC for Bayesian inference Illustration of MCMC for the evaluation of expectations with respect to a distribution MCMC for estimation of maxima or minima of ...

... MCMC methods are a collection of techniques that use pseudo-random (computer simulated) values to estimate solutions to mathematical problems MCMC for Bayesian inference Illustration of MCMC for the evaluation of expectations with respect to a distribution MCMC for estimation of maxima or minima of ...

Random Variables and Their Properties (Due 9/18/06)

... For each set evaluate the sample mean and standard deviation of X. Plot a graph of sample mean and sample standard deviation versus number of months. c) Share and discuss your results of (b) with those of other students in the class. Obtain results from at least two other students and add their data ...

... For each set evaluate the sample mean and standard deviation of X. Plot a graph of sample mean and sample standard deviation versus number of months. c) Share and discuss your results of (b) with those of other students in the class. Obtain results from at least two other students and add their data ...

Final Exam Review Vocabulary Sheet

... You don’t have to have the exact definitions of these terms memorized, but you should understand and be able to explain in your own words the concepts represented here. Also, you should understand what context these terms show up in and what calculations/methods are associated with them. bar graph l ...

... You don’t have to have the exact definitions of these terms memorized, but you should understand and be able to explain in your own words the concepts represented here. Also, you should understand what context these terms show up in and what calculations/methods are associated with them. bar graph l ...

# Expectation–maximization algorithm

In statistics, an expectation–maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.