Hidden state models

Hidden state models

FeedbackParticleFilters.HiddenStateModel — Type.
HiddenStateModel{S, T<:TimeType} <: AbstractModel{S}

Abstract type for any model of the hidden state of type S.

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FeedbackParticleFilters.state_dim — Function.
state_dim(problem::AbstractFilteringProblem)

Return the dimensionality of the hidden state in problem.

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state_dim(model::HiddenStateModel)

Returns the dimensionality of the hidden state in model.

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FeedbackParticleFilters.state_type — Function.
state_type(arg)

Returns the type of the hidden state in arg. Supported argument types:

  • Any subtype of AbstractModel
  • Any subtype of AbstractFilteringProblem
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state_type(model::HiddenStateModel)

Returns the data type of the hidden state in model.

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Missing docstring.

Missing docstring for time_type. Check Documenter's build log for details.

FeedbackParticleFilters.initial_condition — Function.
initial_condition(model::HiddenStateModel)

Returns the specification of the initial condition in model. This is either a fixed value or a samplable distribution.

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FeedbackParticleFilters.initialize — Function.
initialize(model::HiddenStateModel)

Returns a sample from the initial distribution of model.

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FeedbackParticleFilters.propagate! — Function.
propagate!(state[s], model::HiddenStateModel[, dt])

Propagates the state(s) according to the model. For ContinuousTime' models, a time stepdt' has to be provided. Multiple states are given as a matrix with columns corresponding to states, and are processed i.i.d.

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propagate!(sfs, filtering_problem, filtering_algorithm; dt) --> sfs

Propagates the system and filter states for one time-step according to the specified filtering problem and algorithm.

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Diffusion processes

Missing docstring.

Missing docstring for DiffusionStateModel. Check Documenter's build log for details.

FeedbackParticleFilters.LinearDiffusionStateModel — Type.
LinearDiffusionStateModel(A, B; init)

Returns a linear diffusion process hidden state model $dX_t = A X_t dt + B dW_t$ with appropriately sized matrices $A$ and $B$.

Optional argument init stands for the initial condition of the process, which is either

  • A vector of length n for a fixed (deterministic) initial condition
  • A Distributions.Sampleable type for a random initial condition

If argument init is left out, it is set to either

  • a multivariate normal distribution with covariance matrix set to the stationary variance, if it exists
  • the zero vector
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Missing docstring.

Missing docstring for ScalarDiffusionStateModel. Check Documenter's build log for details.

FeedbackParticleFilters.drift_function — Function.
drift_function(model)

Returns the drift function $f$ of the diffusion model $dX_t = f(X_t)dt + g(X_t)dW_t$.

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FeedbackParticleFilters.diffusion_function — Function.
diffusion_function(model)

Returns the diffusion function $g$ of the diffusion model $dX_t = f(X_t)dt + g(X_t)dW_t$.

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FeedbackParticleFilters.noise_dim — Function.
noise_dim(model)

Returns the dimension of the Brownian motion $W_t$ in the diffusion model $dX_t = f(X_t)dt + g(X_t)dW_t$.

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