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Add a model argument to getparams and setparams!! functions #150

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Oct 27, 2024
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2 changes: 1 addition & 1 deletion Project.toml
Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@ uuid = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
keywords = ["markov chain monte carlo", "probabilistic programming"]
license = "MIT"
desc = "A lightweight interface for common MCMC methods."
version = "5.5.0"
version = "5.5.1"
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[deps]
BangBang = "198e06fe-97b7-11e9-32a5-e1d131e6ad66"
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12 changes: 8 additions & 4 deletions src/AbstractMCMC.jl
Original file line number Diff line number Diff line change
Expand Up @@ -81,23 +81,27 @@ The `MCMCSerial` algorithm allows users to sample serially, with no thread or pr
struct MCMCSerial <: AbstractMCMCEnsemble end

"""
getparams(state[; kwargs...])
getparams(state[, logdensity_function])
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Retrieve the values of parameters from the sampler's `state` as a `Vector{<:Real}`.
"""
function getparams end

"""
setparams!!(state, params)
setparams!!(state, params[, logdensity_function])
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Set the values of parameters in the sampler's `state` from a `Vector{<:Real}`.

This function should follow the `BangBang` interface: mutate `state` in-place if possible and
return the mutated `state`. Otherwise, it should return a new `state` containing the updated parameters.

Although not enforced, it should hold that `setparams!!(state, getparams(state)) == state`. In another
word, the sampler should implement a consistent transformation between its internal representation
Although not enforced, it should hold that `setparams!!(state, getparams(state)) == state`. In other
words, the sampler should implement a consistent transformation between its internal representation
and the vector representation of the parameter values.

Sometimes, to maintain the consistency of the log density and parameter values, an optional
`logdensity_function` can be provided. This is useful for samplers that need to evaluate the
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log density at the new parameter values.
"""
function setparams!! end
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