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SUMMARY:Computational Neuroscience Journal Club - Flavia Mancini and Finn 
 Ashley
DTSTART:20220308T133000Z
DTEND:20220308T150000Z
UID:TALK171356@talks.cam.ac.uk
CONTACT:Jake Stroud
DESCRIPTION:Please join us for our fortnightly journal club online via zoo
 m where two presenters will jointly present a topic together. The next top
 ic is ‘Bayesian filters for statistical inference of stochasticity and v
 olatility’ presented by Flavia Mancini and Finn Ashley.\n\nZoom informat
 ion:\nhttps://us02web.zoom.us/j/84958321096?pwd=dFpsYnpJYWVNeHlJbEFKbW1OTz
 FiQT09\nMeeting ID: 849 5832 1096\nPasscode: 506576\n\nSummary:\nUncertain
 ty influences behaviour by shaping statistical inference and learning. Unc
 ertainty can relate to both the volatility and stochasticity of an outcome
 . For simplicity\, computational models of statistical inference often est
 imate only either volatility or stochasticity. However\, this simplificati
 on can lead to erroneous interpretations because volatility and stochastic
 ity are interdependent. \nWe consider and compare two statistical inferenc
 e 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 theoretica
 l basis of Bayesian filters\, contrasting Kalman and particle filtering ap
 proaches (with focus on the Rao-Blackwellized particle filtering). We will
  conclude with examples of behavioural applications of these models. \n\nR
 eferences:\n\nPiray\, P.\, Daw\, N.D. A model for learning based on the jo
 int estimation of stochasticity and volatility. Nat Commun 12\, 6587 (2021
 ). https://doi.org/10.1038/s41467-021-26731-9 \n \nPiray 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 \n \nDoucet\, 
 A.\, Godsill\, S. & Andrieu\, C. On sequential Monte Carlo sampling method
 s for Bayesian filtering. Statistics and Computing 10\, 197–208 (2000). 
 https://doi.org/10.1023/A:1008935410038 
LOCATION:Online on Zoom
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