New Special Issue "Bayesian Inference for Psychology and Psychiatry" is Open for Submission!

Guest Editors: Dr. Rutger Goekoop, Dr. Paul Badcock and Dr. Adam Safron

Submit to the Special Issue: https://www.mdpi.com/journal/entropy/special_issues/45RI6K2F78

Submission deadline: 20 January 2025

Special Issue Information: Bayesian inference as a statistical tool is on the rise and bound to change psychology and psychiatry for many decades to come. Its systematic incorporation of prior knowledge in estimating the posterior probability of some outcome is arguably the most optimal way of dealing with information in clinical practice, where bits and pieces of new information come in each day and shed a new light on our diagnostic models. The mild mischief of model inversion allows a tentative glance at a hidden world that probably caused the diagnostic data. Whether using variational inference to iteratively update diagnostic and prognostic models based on continuously gathered data, Bayesian classification to classify patients, or knowledge graphs to predict clinical outcomes from unstructured and disparate data, Bayesian inference has the cards to guarantee optimal use of the rich datasets gathered in clinical settings.

Across the globe, exact or approximate Bayesian methods are used in areas as diverse as statistical physics, machine learning, systems biology, neuroscience, and clinical medicine, where Bayes’ rule governs the equations of motion that are required to model the evolution of living as well as non-living systems across timescales. At the heart of such dynamics is the Free Energy Principle, which defines how optimal predictive models of some system depend on a balancing act between model accuracy and complexity, or, equivalently, model energy and entropy. This ties immediately to active inference and the Bayesian brain hypothesis, where subjects iteratively act to change their worlds to sample the right kind of sensory data and optimize their predictive models of the world, which in turn inspire action. In this view, all subjective experience is ‘just a model’ that serves as the best explanation that subjects can give for their sensory events, and problems of (active) inference define mental health problems.

In this Special Issue, we examine the application of Bayesian methods to the field of clinical psychology and psychiatry. Of special interest are the use of Bayesian methods in the diagnosis and treatment of mental health problems in clinical practice, Bayesian inference in network analyses of experience sampling data (ESM), as well as theoretical papers and neuroimaging studies that focus on active inference to understand the nature and dynamics of mental health and its problems. We welcome contributions from diverse fields of science to promote interdisciplinary dialogue and a fruitful exchange of ideas.