| Copyright | (c) Adam Scibior 2015-2020 |
|---|---|
| License | MIT |
| Maintainer | leonhard.markert@tweag.io |
| Stability | experimental |
| Portability | GHC |
| Safe Haskell | None |
| Language | Haskell2010 |
Control.Monad.Bayes.Inference.RMSMC
Description
Resample-move Sequential Monte Carlo (RM-SMC) sampling.
Walter Gilks and Carlo Berzuini. 2001. Following a moving target - Monte Carlo inference for dynamic Bayesian models. Journal of the Royal Statistical Society 63 (2001), 127-146. http://www.mathcs.emory.edu/~whalen/Papers/BNs/MonteCarlo-DBNs.pdf
Synopsis
- rmsmc :: MonadSample m => Int -> Int -> Int -> Sequential (Traced (Population m)) a -> Population m a
- rmsmcLocal :: MonadSample m => Int -> Int -> Int -> Sequential (Traced (Population m)) a -> Population m a
- rmsmcBasic :: MonadSample m => Int -> Int -> Int -> Sequential (Traced (Population m)) a -> Population m a
Documentation
Arguments
| :: MonadSample m | |
| => Int | number of timesteps |
| -> Int | number of particles |
| -> Int | number of Metropolis-Hastings transitions after each resampling |
| -> Sequential (Traced (Population m)) a | model |
| -> Population m a |
Resample-move Sequential Monte Carlo.
Arguments
| :: MonadSample m | |
| => Int | number of timesteps |
| -> Int | number of particles |
| -> Int | number of Metropolis-Hastings transitions after each resampling |
| -> Sequential (Traced (Population m)) a | model |
| -> Population m a |
A variant of resample-move Sequential Monte Carlo where only random variables since last resampling are considered for rejuvenation.
Arguments
| :: MonadSample m | |
| => Int | number of timesteps |
| -> Int | number of particles |
| -> Int | number of Metropolis-Hastings transitions after each resampling |
| -> Sequential (Traced (Population m)) a | model |
| -> Population m a |
Resample-move Sequential Monte Carlo with a more efficient tracing representation.