Show pageOld revisionsBacklinksFold/unfold allBack to top This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong. ====== mcmc ====== http://chi-feng.github.io/mcmc-demo/ $ E_\pi[T(X)] = \int T(x)\pi(x) dx. $ In Bayesian inference, we are interested in posterior mean $E(\theta|y)$ or posterior variance $Var(\theta|y)$. One solution is to draw independent samples $ ( X^{(1)}, X^{(2)}, \cdots, X^{(N)} )$ from $\pi(x)$, then we can approximate $ E_\pi[T(X)] \approx \frac{1}{N} \sum_{t=1}^N T( X^{(t) }) $ Law of large numbers -> 위 근사는 adoptable it is known that above approximation is still possible if we sample using a Markov chain. This is the main idea of MCMC method. <code> code </code> {{tag>data_analysis tag1 tag2}} ~~DISCUSSION~~ data_analysis/mcmc.txt Last modified: 2025/07/07 14:12by 127.0.0.1