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About Lecture Notes On Regression: Markov Chain Monte Carlo (MCMC)
1. Markov Chain Monte Carlo (MCMC) is a method for sampling from probability distributions using Markov chains. It is commonly used for regression analysis and estimating parameters in Bayesian statistics. 2. MCMC works by constructing a Markov chain that has the desired distribution as its equilibrium distribution. It then generates samples by simulating transitions in the chain. 3. The samples from the Markov chain are used to estimate properties of the desired distribution such as the mean or quantiles. The more samples that are generated, the better the estimates.
Detailed Information
| Author: | ['Ashwin Venkatesan'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 13 |
| Language: | English |
| Format: | |
| Price: | FREE |
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