Online simulator-based experimental design for cognitive model selection

1 code implementation • 3 Mar 2023 • Alexander Aushev, Aini Putkonen, Gregoire Clarte, Suyog Chandramouli, Luigi Acerbi, Samuel Kaski, Andrew Howes

In this paper, we propose BOSMOS: an approach to experimental design that can select between computational models without tractable likelihoods.

Experimental Design Model Selection

Likelihood-Free Inference in State-Space Models with Unknown Dynamics

2 code implementations • NeurIPS Workshop Deep_Invers 2021 • Alexander Aushev, Thong Tran, Henri Pesonen, Andrew Howes, Samuel Kaski

Likelihood-free inference (LFI) has been successfully applied to state-space models, where the likelihood of observations is not available but synthetic observations generated by a black-box simulator can be used for inference instead.

Likelihood-Free Inference with Deep Gaussian Processes

1 code implementation • 18 Jun 2020 • Alexander Aushev, Henri Pesonen, Markus Heinonen, Jukka Corander, Samuel Kaski

In recent years, surrogate models have been successfully used in likelihood-free inference to decrease the number of simulator evaluations.

Bayesian Optimization Gaussian Processes