Minh-Ngoc Tran

Variational Bayes on manifolds

Publisher: Springer Science and Business Media LLC

Date: 14-09-2021

DOI: 10.1007/S11222-021-10047-1

Efficient selection between hierarchical cognitive models: Cross-validation with variational Bayes.

Publisher: American Psychological Association (APA)

Date: 21-04-2022

DOI: 10.1037/MET0000458

Abstract: Model comparison is the cornerstone of theoretical progress in psychological research. Common practice overwhelmingly relies on tools that evaluate competing models by balancing in-s le descriptive adequacy against model flexibility, with modern approaches advocating the use of marginal likelihood for hierarchical cognitive models. Cross-validation is another popular approach but its implementation remains out of reach for cognitive models evaluated in a Bayesian hierarchical framework, with the major hurdle being its prohibitive computational cost. To address this issue, we develop novel algorithms that make variational Bayes (VB) inference for hierarchical models feasible and computationally efficient for complex cognitive models of substantive theoretical interest. It is well known that VB produces good estimates of the first moments of the parameters, which gives good predictive densities estimates. We thus develop a novel VB algorithm with Bayesian prediction as a tool to perform model comparison by cross-validation, which we refer to as CVVB. In particular, CVVB can be used as a model screening device that quickly identifies bad models. We demonstrate the utility of CVVB by revisiting a classic question in decision making research: what latent components of processing drive the ubiquitous speed-accuracy tradeoff? We demonstrate that CVVB strongly agrees with model comparison via marginal likelihood, yet achieves the outcome in much less time. Our approach brings cross-validation within reach of theoretically important psychological models, making it feasible to compare much larger families of hierarchically specified cognitive models than has previously been possible. To enhance the applicability of the algorithm, we provide Matlab code together with a user manual so users can easily implement VB and/or CVVB for the models considered in this article and their variants. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Time-evolving psychological processes over repeated decisions.

Publisher: American Psychological Association (APA)

Date: 04-2022

DOI: 10.1037/REV0000351

Abstract: Many psychological experiments have subjects repeat a task to gain the statistical precision required to test quantitative theories of psychological performance. In such experiments, time-on-task can have sizable effects on performance, changing the psychological processes under investigation. Most research has either ignored these changes, treating the underlying process as static, or sacrificed some psychological content of the models for statistical simplicity. We use particle Markov chain Monte-Carlo methods to study psychologically plausible time-varying changes in model parameters. Using data from three highly cited experiments, we find strong evidence in favor of a hidden Markov switching process as an explanation of time-varying effects. This embodies the psychological assumption of "regime switching," with subjects alternating between different cognitive states representing different modes of decision-making. The switching model explains key long- and short-term dynamic effects in the data. The central idea of our approach can be applied quite generally to quantitative psychological theories, beyond the models and datasets that we investigate. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Computationally efficient Bayesian estimation of high-dimensional Archimedean copulas with discrete and mixed margins

Publisher: Springer Science and Business Media LLC

Date: 10-12-2019

DOI: 10.1007/S11222-018-9846-Y

Vietnam National University University Of Science

Location: Viet Nam

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University Of Sydney Business School

Location: Australia

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University Of New South Wales

Location: Australia

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National University Of Singapore

Location: Singapore

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Discovery Projects - Grant ID: DP210103873

Start Date: 07-2021

End Date: 12-2024

Amount: $378,500.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP180102195

Start Date: 2018

End Date: 09-2023

Amount: $348,912.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP200103015

Start Date: 02-2020

End Date: 12-2024

Amount: $280,000.00

Funder: Australian Research Council

Industrial Transformation Training Centres - Grant ID: IC190100031

Start Date: 08-2020

End Date: 08-2025

Amount: $3,973,202.00

Funder: Australian Research Council