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@jemoka / Jemoka Knowledge Base / raw/concept/kbhfailure_distribution.md
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--- title: "Failure Distribution" source: https://www.jemoka.com/posts/kbhfailure_distribution/ --- For a trajectory \(p\qty(\tau)\), the failure distribution is $p \qty(τ | τ ¬ ∈ψ)$—the probability of a particular trajectory given that its a failure: \begin{equation} p \qty( \tau \mid \tau \not \in \psi) = \frac{\mathbb{1}\qty {\tau \not \in \psi} p\qty(\tau)}{ \int \mathbb{1}\qty {\tau \not \in \psi} p\qty(\tau) \dd{\tau}} \end{equation} This bottom integral could be very difficult to compute; but the numerator may take a bit more work to compute! So ultimately we can also give up and don’t normalize (and then use systems that allows us to draw samples from unnormalized probability densities: \begin{equation} \hat{p} \qty( \tau \mid \tau \not \in \psi) = {\mathbb{1}\qty {\tau \not \in \psi} p\qty(\tau)} \end{equation} so we can implicitly represents the failure distirbution using the drawn samples. some ways of sampling from failure distribution Rejection Sampling Markov Chain Monte-Carlo