Proposal distribution

At the current state $x$, a Metropolis–Hastings algorithm draws a candidate state $y$ from its proposal distribution $q(\cdot \mid x)$. The Metropolis–Hastings acceptance probability corrects the proposal's bias so that the target distribution remains invariant.

Let $0<\beta \le 1$, let $X\in {\mathbb{R}}^p$, and proposeThe proposal distribution is $N(\sqrt{1-{\beta }^2}X,{\beta }^2{I}_p)$. If $X\sim N(0,I_p)$ independently of $Z$, then $(X,Y)$ is a jointly multivariate normal distribution invariant under exchanging $X$ and $Y$, because both random vectors have covariance matrix $I_p$ and their cross-covariance matrices are both $\sqrt{1-{\beta }^2}{I}_p$. Consequently its density $q$ satisfieswhere $\varphi$ is the standard-normal density. Thus the proposal is reversible with respect to the standard normal distribution.

The preconditioned Crank–Nicolson algorithm, or pCN algorithm, uses a Gaussian autoregressive proposal reversible with respect to a standard normal distribution in a Metropolis–Hastings algorithm. When the target density is a likelihood times the proposal's invariant Gaussian prior density, the Gaussian factors cancel from the Metropolis–Hastings acceptance probability, leaving the likelihood ratio.