Autologistic binary-image model
= Autologistic binary-image model
{title2=$p(x)\propto e^{\alpha\sum_vx_v+\beta\sum_{\{v,w\}}x_vx_w}$}
On a graph, a binary configuration has probability proportional to $\exp(\alpha\sum_v x_v+\beta\sum_{\{v,w\}}x_vx_w)$, with each edge counted once. The full <conditional probability> of a one is logistic with <linear predictor> $\alpha+\beta\sum_{w\sim v}x_w$. Local neighbor sums eliminate the need to evaluate the <normalizing constant>.