Optimal Bayesian design for model discrimination via classification

09/14/2018
by   Markus Hainy, et al.
0

Performing optimal Bayesian design for discriminating between competing models is computationally intensive as it involves estimating posterior model probabilities for thousands of simulated datasets. This issue is compounded further when the likelihood functions for the rival models are computationally expensive. A new approach using supervised classification methods is developed to perform Bayesian optimal model discrimination design. This approach requires considerably fewer simulations from the candidate models than previous approaches using approximate Bayesian computation. Further, it is easy to assess the performance of the optimal design through the misclassification matrix. The approach is particularly useful in the presence of models with intractable likelihoods but can also provide computational advantages when the likelihoods are manageable.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 16

10/23/2018

Efficient Bayesian Experimental Design for Implicit Models

Bayesian experimental design involves the optimal allocation of resource...
03/15/2022

Model Comparison in Approximate Bayesian Computation

A common problem in natural sciences is the comparison of competing mode...
03/18/2018

Approximating the Likelihood in Approximate Bayesian Computation

This chapter will appear in the forthcoming Handbook of Approximate Baye...
10/29/2021

Bayesian Optimal Experimental Design for Simulator Models of Cognition

Bayesian optimal experimental design (BOED) is a methodology to identify...
04/22/2020

Amortized Bayesian model comparison with evidential deep learning

Comparing competing mathematical models of complex natural processes is ...
02/12/2018

Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches

Healthcare companies must submit pharmaceutical drugs or medical devices...
11/25/2020

Surrogate-based Bayesian Comparison of Computationally Expensive Models: Application to Microbially Induced Calcite Precipitation

Geochemical processes in subsurface reservoirs affected by microbial act...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.