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GitHub

API

VarNames

AbstractPPL.VarName — Type
VarName{sym}(optic=identity)

A variable identifier for a symbol sym and optic optic.

The Julia variable in the model corresponding to sym can refer to a single value or to a hierarchical array structure of univariate, multivariate or matrix variables. The field lens stores the indices requires to access the random variable from the Julia variable indicated by sym as a tuple of tuples. Each element of the tuple thereby contains the indices of one optic operation.

VarNames can be manually constructed using the VarName{sym}(optic) constructor, or from an optic expression through the @varname convenience macro.

Examples

julia> vn = VarName{:x}(Accessors.IndexLens((Colon(), 1)) ⨟ Accessors.IndexLens((2, )))
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GitHub
API
VarNames
AbstractPPL.VarName — Type
VarName{sym}(optic=identity)
A variable identifier for a symbol sym and optic optic.
The Julia variable in the model corresponding to sym can refer to a single value or to a hierarchical array structure of univariate, multivariate or matrix variables. The field lens stores the indices requires to access the random variable from the Julia variable indicated by sym as a tuple of tuples. Each element of the tuple thereby contains the indices of one optic operation.
VarNames can be manually constructed using the VarName{sym}(optic) constructor, or from an optic expression through the @varname convenience macro.
Examples
julia> vn = VarName{:x}(Accessors.IndexLens((Colon(), 1)) ⨟ Accessors.IndexLens((2, )))
 x[:, 1][2]
 
 julia> getoptic(vn)
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 julia> y = ones(2); vn_to_string(@varname(y[:], true))
 "(sym = \"y\", optic = (type = \"index\", indices = \"(ConcretizedSlice(Base.OneTo(2)),)\"))"
source
AbstractPPL.vn_from_string — Function
vn_from_string(str)
Convert a string representation of a VarName back to a VarName. The string should have been generated by vn_to_string.
Warning
This function should only be used with trusted input, as it uses eval
and Meta.parse to parse the string.
source
Abstract model functions
AbstractPPL.condition — Function
condition(model, observations)
Condition the generative model model on some observed data, creating a new model of the (possibly unnormalized) posterior distribution over them.
observations can be of any supported internal trace type, or a fixed probability expression.
The invariant 
m = decondition(condition(m, obs))
should hold for generative models m and arbitrary obs.
source
AbstractPPL.decondition — Function
decondition(conditioned_model)
Remove the conditioning (i.e., observation data) from conditioned_model, turning it into a generative model over prior and observed variables.
The invariant 
m == condition(decondition(m), obs)
should hold for models m with conditioned variables obs.
source
DensityInterface.logdensityof — Function
logdensityof(model, trace)
Evaluate the (possibly unnormalized) density of the model specified by the probabilistic program in model, at specific values for the random variables given through trace.
trace can be of any supported internal trace type, or a fixed probability expression.
logdensityof should interact with conditioning and deconditioning in the way required by probability theory.
source
Abstract traces

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-Home · AbstractPPL
GitHub
AbstractPPL.jl
A lightweight package containing interfaces and associated APIs for modelling languages for probabilistic programming.

+Home · AbstractPPL
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GitHub
AbstractPPL.jl
A lightweight package containing interfaces and associated APIs for modelling languages for probabilistic programming.

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