| Copyright | (c) Adam Scibior 2015-2020 |
|---|---|
| License | MIT |
| Maintainer | leonhard.markert@tweag.io |
| Stability | experimental |
| Portability | GHC |
| Safe Haskell | Safe-Inferred |
| Language | Haskell2010 |
Control.Monad.Bayes.Inference.SMC2
Description
Sequential Monte Carlo squared (SMC²) sampling.
Nicolas Chopin, Pierre E. Jacob, and Omiros Papaspiliopoulos. 2013. SMC²: an efficient algorithm for sequential analysis of state space models. Journal of the Royal Statistical Society Series B: Statistical Methodology 75 (2013), 397-426. Issue 3. https://doi.org/10.1111/j.1467-9868.2012.01046.x
Synopsis
- smc2 :: MonadDistribution m => Int -> Int -> Int -> Int -> Sequential (Traced (Population m)) b -> (b -> Sequential (Population (SMC2 m)) a) -> Population m [(a, Log Double)]
- data SMC2 m a
Documentation
Arguments
| :: MonadDistribution m | |
| => Int | number of time steps |
| -> Int | number of inner particles |
| -> Int | number of outer particles |
| -> Int | number of MH transitions |
| -> Sequential (Traced (Population m)) b | model parameters |
| -> (b -> Sequential (Population (SMC2 m)) a) | model |
| -> Population m [(a, Log Double)] |
Sequential Monte Carlo squared.
Helper monad transformer for preprocessing the model for smc2.