[[
wikihub
]]
Search
⌘K
Explore
People
For Agents
Sign in
Explore
People
For Agents
Sign in
@jemoka / Jemoka Knowledge Base / raw/concept/kbhprobabilistic_programming.md
Suggest edit
Cancel
Submit suggestion
Title
Name
Note
--- title: "probabilistic programming" source: https://www.jemoka.com/posts/kbhprobabilistic_programming/ --- Remember Bayes Rule in Baysian Parameter Learning: \begin{equation} P\qty(\theta | D) = \frac{P\qty(D | \theta) p \qty(\theta)}{\int_{\theta}P\qty(D | \theta) p \qty(\theta) \dd{\theta}} \end{equation} we can’t actually easily compute the bottom without taking an analytic integral; instead we can sample from it. If you want analytical form, you should hope that your likelihood function is a conjugate prior which allows us to analytically update prirors.