Parameters

Sampled parameters (e.g., from the prior distribution) are typically stored as a   matrix (possibly with named rows; see NamedMatrix), where is the dimension of the parameter vector of interest and is the number of sampled parameter vectors. However, any batchable object (compatible with numobs/getobs) is supported.

It can sometimes be helpful to wrap the parameters in a user-defined type that also stores expensive intermediate objects needed for simulating data (e.g., Cholesky factors). The user-defined type should be a subtype of AbstractParameterSet, whose only requirement is a field θ that stores parameters.

NeuralEstimators.AbstractParameterSet Type

AbstractParameterSet

An abstract supertype for user-defined types that store parameters and any auxiliary objects needed for data simulation.

The user-defined type must have a field θ that stores the parameters. Typically, θ is a × matrix, where is the dimension of the parameter vector and is the number of sampled parameter vectors, though any batchable object compatible with numobs/getobs is supported. There are no other requirements.

The number of parameter instances can be retrieved with numobs, and the size of θ can be inspected with size.

Subtypes of AbstractParameterSet support indexing via Base.getindex, with any batchable fields subsetted accordingly and all other fields left unchanged. To modify this default behaviour, provide a specific Base.getindex method for your concrete subtype.

Examples

struct Parameters <: AbstractParameterSet
	θ
	# auxiliary objects needed for data simulation
end

θ = randn(2, 100)
parameters = Parameters(θ)
numobs(parameters)   # 100
size(parameters)     # (2, 100)
parameters[1:10]     # subset of 10 parameter vectors

source

NamedArrays.NamedMatrix Type

NamedMatrix(; kwargs...)

Construct a named matrix where each keyword argument defines a named row.

Examples

NamedMatrix(μ = randn(3), σ = rand(3))

source

Data

Simulated data sets are stored as mini-batches in a format amenable to the chosen neural-network architecture; the only requirement is compatibility with numobs/getobs. For example, when constructing an estimator from data collected over a two-dimensional grid, one may use a CNN, with each data set stored in the final dimension of a four-dimensional array.

Precomputed (expert) summary statistics can be incorporated by wrapping the simulated data in a DataSet object, which couples the raw data with a matrix of summary statistics.

NeuralEstimators.DataSet Type

DataSet(Z, S)
DataSet(Z)

A container that couples raw data Z with precomputed expert summary statistics S (a matrix with one column per data set). Passing a DataSet to any neural estimator causes the summary network to be applied to Z, with the resulting learned summary statistics concatenated with S before being passed to the inference network:

Since S is precomputed and stored as a plain matrix, no special treatment is needed during training: gradients do not flow through S.

If S is not provided, DataSet(Z) is equivalent to passing Z directly.

See also summarystatistics.

Examples

using NeuralEstimators
using Statistics: mean, var

# Simulate data: Z|μ,σ ~ N(μ, σ²)
n, m, K = 1, 50, 500
θ = rand(2, K)
Z = [θ[1, k] .+ θ[2, k] .* randn(n, m) for k in 1:K]

# Precompute expert summary statistics (e.g., sample mean and variance)
S = hcat([vcat(mean(z), var(z)) for z in Z]...)

# Package into a DataSet object
datasets = DataSet(Z, S)

source