Copyright | (c) Adam Scibior 2015-2020 |
---|---|
License | MIT |
Maintainer | leonhard.markert@tweag.io |
Stability | experimental |
Portability | GHC |
Safe Haskell | None |
Language | Haskell2010 |
Sequential Monte Carlo (SMC) sampling.
Arnaud Doucet and Adam M. Johansen. 2011. A tutorial on particle filtering and smoothing: fifteen years later. In The Oxford Handbook of Nonlinear Filtering, Dan Crisan and Boris Rozovskii (Eds.). Oxford University Press, Chapter 8.
Synopsis
- sir :: Monad m => (forall x. Population m x -> Population m x) -> Int -> Int -> Sequential (Population m) a -> Population m a
- smcMultinomial :: MonadSample m => Int -> Int -> Sequential (Population m) a -> Population m a
- smcSystematic :: MonadSample m => Int -> Int -> Sequential (Population m) a -> Population m a
- smcMultinomialPush :: MonadInfer m => Int -> Int -> Sequential (Population m) a -> Population m a
- smcSystematicPush :: MonadInfer m => Int -> Int -> Sequential (Population m) a -> Population m a
Documentation
:: Monad m | |
=> (forall x. Population m x -> Population m x) | resampler |
-> Int | number of timesteps |
-> Int | population size |
-> Sequential (Population m) a | model |
-> Population m a |
Sequential importance resampling. Basically an SMC template that takes a custom resampler.
:: MonadSample m | |
=> Int | number of timesteps |
-> Int | number of particles |
-> Sequential (Population m) a | model |
-> Population m a |
Sequential Monte Carlo with multinomial resampling at each timestep. Weights are not normalized.
:: MonadSample m | |
=> Int | number of timesteps |
-> Int | number of particles |
-> Sequential (Population m) a | model |
-> Population m a |
Sequential Monte Carlo with systematic resampling at each timestep. Weights are not normalized.
:: MonadInfer m | |
=> Int | number of timesteps |
-> Int | number of particles |
-> Sequential (Population m) a | model |
-> Population m a |
Sequential Monte Carlo with multinomial resampling at each timestep. Weights are normalized at each timestep and the total weight is pushed as a score into the transformed monad.
:: MonadInfer m | |
=> Int | number of timesteps |
-> Int | number of particles |
-> Sequential (Population m) a | model |
-> Population m a |
Sequential Monte Carlo with systematic resampling at each timestep. Weights are normalized at each timestep and the total weight is pushed as a score into the transformed monad.