Safe Haskell	Safe-Inferred
Language	Haskell2010

FRP.Rhine.Bayes

Contents

Synopsis

Inference methods

Arguments

:: forall m cl a b. Monad m
=> Int	Number of particles
-> (forall x. Population m x -> Population m x)	Resampler (see `Population` for some standard choices)
-> ClSF (Population m) cl a b	A signal function modelling the stochastic process on which to perform inference. `a` represents observations upon which the model should condition, using e.g. `score`. It can also additionally contain hyperparameters. `b` is the type of estimated current state.
-> ClSF m cl a [(b, Log Double)]

Run the Sequential Monte Carlo algorithm continuously on a ClSF.

Short standard library of stochastic processes

White noise, that is, an independent normal distribution at every time step.

Arguments

:: (MonadDistribution m, VectorSpace v (Diff td))
=> (Diff td -> m v)	The increment function at every time step. The argument is the difference between times.
-> Behaviour m td v

Construct a Lévy process from the increment between time steps.

Arguments

:: (MonadDistribution m, Diff td ~ Double)
=> Diff td	Time scale of variance.
-> Behaviour m td Double

The Wiener process, also known as Brownian motion.

Arguments

:: (MonadDistribution m, Diff td ~ Double)
=> Diff td	Time scale of variance.
-> Behaviour m td Double

The Wiener process, also known as Brownian motion.

The Wiener process, also known as Brownian motion, with varying variance parameter.

The Wiener process, also known as Brownian motion, with varying variance parameter.

Arguments

:: (MonadDistribution m, Diff td ~ Double)
=> Diff td	Time scale of variance
-> Behaviour m td (Log Double)

The wiener process transformed to the Log domain, also called the geometric Wiener process.