Tau-leaped particle learning is a sequential Monte Carlo (SMC) approach to Bayesian inference in a Poisson-binomial state-space model, ie. Poisson transitions and binomial observations on those transitions. Tau-leaping provides a discrete approximation to a continuous-time process and particle utilizes analytical tractabilities of the model to provide an efficient algorithm.
|Maintainer||Jarad Niemi <[email protected]>|
|Package repository||View on GitHub|
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