BSM{T<:Real} <: AbstractBSM

A model object for the Bayesian sampler.

Parameters

• θs::AbstractArray{T,N}: an array of true subjective probabilities. The array can be of any size and dimension.
• β::T: parameter for symmetrical beta prior
• n::Int: number of samples for marginal and conditional events
• n′::Int: number of samples for conjunctions and disjunctions where n′ ≤ n

Constructors

BSM(Θs, β, n, n′)

BSM(; Θs, β, n, n′)

Example

using BayesianSamplerModel
Θs = [.25 .15; .3 .3]
model = BSM(; Θs, β=1.0, n=20, n′=10)
p_a = compute_marginal(model, sum(Θs[1,:]))
p_ab = compute_joint(model, Θs[1])
p_agb = compute_conditional(model, sum(Θs[1]) / sum(Θs[:,1]))

References

Zhu, J. Q., Sanborn, A. N., & Chater, N. (2020). The Bayesian sampler: Generic Bayesian inference causes incoherence in human probability judgments. Psychological Review, 127(5), 719.

compute_conditional(m::AbstractBSM, θ)

Compute the mean conditional posterior probability judgment for the Bayesian Sampler Model.

Arguments

• m::AbstractBSM: an object for the Bayesian Sampler Model
• θ: the true subjective probability

compute_joint(m::AbstractBSM, θ)

Compute the mean marginal posterior probability judgment for the Bayesian Sampler Model.

Arguments

• m::AbstractBSM: an object for the Bayesian Sampler Model
• θ: the true subjective probability

compute_marginal(m::AbstractBSM, θ)

Compute the mean marginal posterior probability judgment for the Bayesian Sampler Model.

Arguments

• m::AbstractBSM: an object for the Bayesian Sampler Model
• θ: the true subjective probability