Abstract

Please join us for our fortnightly journal club online via zoom where two presenters will jointly present a topic together. The next topic is ‘Bayesian filters for statistical inference of stochasticity and volatility’ presented by Flavia Mancini and Finn Ashley.

Zoom information: https://us02web.zoom.us/j/84958321096?pwd=dFpsYnpJYWVNeHlJbEFKbW1OTzFiQT09 Meeting ID: 849 5832 1096 Passcode: 506576

Summary: Uncertainty influences behaviour by shaping statistical inference and learning. Uncertainty can relate to both the volatility and stochasticity of an outcome. For simplicity, computational models of statistical inference often estimate only either volatility or stochasticity. However, this simplification can lead to erroneous interpretations because volatility and stochasticity are interdependent. We consider and compare two statistical inference models that describe learning to predict volatile, noisy outcomes: (1) a volatile Kalman Filter model that estimates volatility (Piray & Daw 2020) and (2) a Kalman-Filter model that uses a particle filter for the joint estimation of volatility and stochasticity. We will discuss the theoretical basis of Bayesian filters, contrasting Kalman and particle filtering approaches (with focus on the Rao-Blackwellized particle filtering). We will conclude with examples of behavioural applications of these models.

References:

Piray, P., Daw, N.D. A model for learning based on the joint estimation of stochasticity and volatility. Nat Commun 12, 6587 (2021). https://doi.org/10.1038/s41467-021-26731-9

Piray P, Daw ND (2020) A simple model for learning in volatile environments. PLoS Comput Biol 16(7): e1007963. https://doi.org/10.1371/journal.pcbi.1007963

Doucet, A., Godsill, S. & Andrieu, C. On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing 10, 197–208 (2000). https://doi.org/10.1023/A:1008935410038