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MCBayes.jl

MCBayes.jl is Julia package for Bayesian inference, which only depends on Julia standard library packages.

MCBayes.Stan — Type
Stan(dims, chains, T = Float64; kwargs...)

Initialize Stan sampler object. The number of dimensions dims and number of chains chains are the only required arguments. The type T of the ...

Optionally, via keyword arguments, can set the metric, stepsize, seed, maxtreedepth, and maxdeltaH.

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Missing docstring.

Missing docstring for sample!. Check Documenter's build log for details.

MCBayes.StepsizeDualAverage — Type
StepsizeDualAverage(initial_stepsize::Vector)

Construct a stepsize adapter using the dual averaging method by Nesterov 2009, as used in Stan. The length of initial_stepsize::Vector must be appropriate for the sampling algorithm for which this stepsize adapter will be used.

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MCBayes.StepsizeConstant — Type
StepsizeConstant(initial_stepsize::Vector)

Construct a stepsize adapter for which the stepsize is fixed at it's initial value.

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MCBayes.OnlineMoments — Type
OnlineMoments(d, c, update = true)

Returns an OnlineMoments struct with mean and variance Matrixes of size (d, c). When update!(om::OnlineMoments, x::Matrix) is called, update determines whether or not any updates will actually be applied.

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MCBayes.update! — Function

Adam update.

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update!(om::OnlineMoments, x::AbstractMatrix; kwargs...)

Update om's mean and variance Matrixes with the data contained in x. The rows of x and om.m (and thus om.v) must match. The columns of x and om.m must either match or om.m must have only 1 column. In the latter case, all columns of x will be used to update the same moments om.m and om.v.

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Assumes x is centered and scaled

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Centers x::AbstractVector using location and scale

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Centers x using location and scale

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Adam update on log-scale.

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