The tenets of quantile-based inference in Bayesian models

Perepolkin, Dmytro; Goodrich, Benjamin; Sahlin, Ullrika

doi:10.1016/j.csda.2023.107795

Cited by 4 publications

(1 citation statement)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Brute-Force MAP algorithm is mainly based on the maximum posterior probability to obtain the event hypothesis under known conditions [17][18][19]. Suppose H is a finite hypothesis space defined on the instance space D.…”

Section: Minimum Description Lengthmentioning

confidence: 99%

Formulation and Implementation of a Bayesian Network-Based Model

Shi

2023

Int. J. Appl. Inf. Manag.

View full text Add to dashboard Cite

At present, Bayesian networks lack consistent algorithms that support structure establishment, parameter learning, and knowledge reasoning, making it impossible to connect knowledge establishment and application processes. In view of this situation, by designing a genetic algorithm coding method suitable for Bayesian network learning, crossover and mutation operators with adjustment strategies, the fitness function for reasoning error feedback can be carried out. Experimental results show that the new algorithm not only simultaneously optimizes the network structure and parameters, but also can adaptively learn and correct inference errors, and has a more satisfactory knowledge inference accuracy rate.

show abstract

Section: Minimum Description Lengthmentioning

confidence: 99%

Formulation and Implementation of a Bayesian Network-Based Model

Shi

2023

Int. J. Appl. Inf. Manag.

View full text Add to dashboard Cite

show abstract

Hybrid elicitation and quantile-parametrized likelihood

Perepolkin,

Goodrich,

Sahlin

2023

Stat Comput

View full text Add to dashboard Cite

This paper extends the application of quantile-based Bayesian inference to probability distributions defined in terms of quantiles of observable quantities. Quantile-parameterized distributions are characterized by high shape flexibility and parameter interpretability, making them useful for eliciting information about observables. To encode uncertainty in the quantiles elicited from experts, we propose a Bayesian model based on the metalog distribution and a variant of the Dirichlet prior. We discuss the resulting hybrid expert elicitation protocol, which aims to characterize uncertainty in parameters by asking questions about observable quantities. We also compare and contrast this approach with parametric and predictive elicitation methods.

show abstract

Bayesian estimation of a quantile-based factor model

Redivo,

Viroli

2024

Journal of Statistical Computation and Simulation

View full text Add to dashboard Cite

The tenets of quantile-based inference in Bayesian models

Cited by 4 publications

References 73 publications

Formulation and Implementation of a Bayesian Network-Based Model

Formulation and Implementation of a Bayesian Network-Based Model

Hybrid elicitation and quantile-parametrized likelihood

Bayesian estimation of a quantile-based factor model

Contact Info

Product

Resources

About