diff --git a/index.html b/index.html index 6a5afc301..3ac259691 100644 --- a/index.html +++ b/index.html @@ -1,2 +1,3 @@ + diff --git a/previews/PR2474/api/Inference/index.html b/previews/PR2474/api/Inference/index.html index 5f7b472e9..4954bd5cd 100644 --- a/previews/PR2474/api/Inference/index.html +++ b/previews/PR2474/api/Inference/index.html @@ -1,5 +1,462 @@ Inference · Turing
GitHub

API: Turing.Inference

Turing.Inference.ESS — Type
ESS

Elliptical slice sampling algorithm.

Examples

julia> @model function gdemo(x)
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            m ~ Normal()
            x ~ Normal(m, 0.5)
        end
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         y[1]  20.0342  20.1188  20.2135  20.2588  20.4188
         y[2]  20.1870  20.3178  20.3839  20.4466  20.5895
 
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 julia> ys_pred = vec(mean(Array(group(predictions, :y)); dims = 1));
 
 julia> sum(abs2, ys_test - ys_pred) ≤ 0.1
@@ -171,3 +627,4 @@
 2-element Array{Array{Float64,1},1}:
  [-2.0844148956440796]
  [-1.704630494695469]
source
+ diff --git a/previews/PR2474/api/Optimisation/index.html b/previews/PR2474/api/Optimisation/index.html index 309c636ab..30963a5fa 100644 --- a/previews/PR2474/api/Optimisation/index.html +++ b/previews/PR2474/api/Optimisation/index.html @@ -1,5 +1,462 @@ Optimisation · Turing
GitHub

API: Turing.Optimisation

SciMLBase.OptimizationProblem — Method
OptimizationProblem(log_density::OptimLogDensity, adtype, constraints)

Create an OptimizationProblem for the objective function defined by log_density.

source
Turing.Optimisation.MAP — Type
MAP <: ModeEstimator

Concrete type for maximum a posteriori estimation. Only used for the Optim.jl interface.

source
Turing.Optimisation.ModeEstimationConstraints — Type
ModeEstimationConstraints

A struct that holds constraints for mode estimation problems.

The fields are the same as possible constraints supported by the Optimization.jl: ub and lb specify lower and upper bounds of box constraints. cons is a function that takes the parameters of the model and returns a list of derived quantities, which are then constrained by the lower and upper bounds set by lcons and ucons. We refer to these as generic constraints. Please see the documentation of Optimization.jl for more details.

Any of the fields can be nothing, disabling the corresponding constraints.

source
Turing.Optimisation.ModeEstimator — Type
ModeEstimator

An abstract type to mark whether mode estimation is to be done with maximum a posteriori (MAP) or maximum likelihood estimation (MLE). This is only needed for the Optim.jl interface.

source
Turing.Optimisation.ModeResult — Type
ModeResult{
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     V<:NamedArrays.NamedArray,
     M<:NamedArrays.NamedArray,
     O<:Optim.MultivariateOptimizationResults,
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     [solver];
     kwargs...
 )

Find the maximum likelihood estimate of a model.

This is a convenience function that calls estimate_mode with MLE() as the estimator. Please see the documentation of Turing.Optimisation.estimate_mode for more details.

source
+ diff --git a/previews/PR2474/api/index.html b/previews/PR2474/api/index.html index 939e38e03..dd203075f 100644 --- a/previews/PR2474/api/index.html +++ b/previews/PR2474/api/index.html @@ -1,5 +1,462 @@ API · Turing
GitHub

API

Module-wide re-exports

Turing.jl directly re-exports the entire public API of the following packages:

Please see the individual packages for their documentation.

For convenience when constructing distributions, Turing.jl also re-exports LinearAlgebra.I.

Individual exports and re-exports

All of the following symbols are exported unqualified by Turing, even though the documentation suggests that many of them are qualified. That means, for example, you can just write

using Turing
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 @model function my_model() end
 
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         y[1]  20.0342  20.1188  20.2135  20.2588  20.4188
         y[2]  20.1870  20.3178  20.3839  20.4466  20.5895
 
-
 julia> ys_pred = vec(mean(Array(group(predictions, :y)); dims = 1));
 
 julia> sum(abs2, ys_test - ys_pred) ≤ 0.1
 true
source

Querying model probabilities and quantities

Please see the generated quantities and probability interface guides for more information.

Exported symbolDocumentationDescription
returnedDynamicPPL.returnedCalculate additional quantities defined in a model
pointwise_loglikelihoodsDynamicPPL.pointwise_loglikelihoodsCompute log likelihoods for each sample in a chain
logpriorDynamicPPL.logpriorCompute log prior probability
logjointDynamicPPL.logjointCompute log joint probability
conditionAbstractPPL.conditionCondition a model on data
deconditionAbstractPPL.deconditionRemove conditioning on data
conditionedDynamicPPL.conditionedReturn the conditioned values of a model
fixDynamicPPL.fixFix the value of a variable
unfixDynamicPPL.unfixUnfix the value of a variable
OrderedDictOrderedCollections.OrderedDictAn ordered dictionary

Point estimates

See the mode estimation tutorial for more information.

Exported symbolDocumentationDescription
maximum_a_posterioriTuring.Optimisation.maximum_a_posterioriFind a MAP estimate for a model
maximum_likelihoodTuring.Optimisation.maximum_likelihoodFind a MLE estimate for a model
MAPTuring.Optimisation.MAPType to use with Optim.jl for MAP estimation
MLETuring.Optimisation.MLEType to use with Optim.jl for MLE estimation
+ diff --git a/previews/PR2474/index.html b/previews/PR2474/index.html index acf85580f..9cf0996b3 100644 --- a/previews/PR2474/index.html +++ b/previews/PR2474/index.html @@ -1,2 +1,460 @@ Home · Turing
GitHub

Turing.jl

This site contains the API documentation for the identifiers exported by Turing.jl.

If you are looking for usage examples and guides, please visit https://turinglang.org/docs.

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