Bayesian inference in motor learning

(ML 7.1) Bayesian inference - A simple example

Illustration of the main idea of Bayesian inference, in the simple case of a univariate Gaussian with a Gaussian prior on the mean (and known variances).

From playlist Machine Learning

How Bayes Theorem works

Part of the End-to-End Machine Learning School Course 191, Selected Models and Methods at https://e2eml.school/191 A walk through a couple of Bayesian inference examples. The blog: http://brohrer.github.io/how_bayesian_inference_works.html The slides: https://docs.google.com/presentatio

From playlist Talks

(ML 7.2) Aspects of Bayesian inference

An informal overview of Bayesian inference, Bayesian procedures, Objective versus Subjective Bayes, Pros/Cons of a Bayesian approach, and priors.

From playlist Machine Learning

6 - Bayes' rule in inference - likelihood

Provides an introduction to Bayesian statistics - in particular the likelihood - by running through a simple example of the application of Bayes' rule to the case of inference over a binary parameter, If you are interested in seeing more of the material, arranged into a playlist, please v

From playlist Bayesian statistics: a comprehensive course

10 - Bayes' rule in inference - example: graphical intuition

This provides a complete example of how Bayes' rule can be used to conduct inference over a discrete parameter. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm Unfortun

From playlist Bayesian statistics: a comprehensive course

7 Bayes' rule in inference the prior and denominator

This provides a short introduction into the use of Bayes' rule in inference, by going through an example where the prior and denominator in the formula are explained. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/play

From playlist Bayesian statistics: a comprehensive course

Stanford Seminar: Concepts and Questions as Programs

EE380: Computer Systems Colloquium Concepts and Questions as Programs Speaker: Brenden Lake, NYU Both AI and cognitive science can gain by studying the human solutions to difficult computational problems [1]. My talk will focus on two problems: concept learning and question asking. Comp

From playlist Stanford EE380-Colloquium on Computer Systems - Seminar Series

Uncertainty in Visuomotor Behavior by Konrad Kording

PROGRAM ICTP-ICTS WINTER SCHOOL ON QUANTITATIVE SYSTEMS BIOLOGY (ONLINE) ORGANIZERS: Vijaykumar Krishnamurthy (ICTS-TIFR, India), Venkatesh N. Murthy (Harvard University, USA), Sharad Ramanathan (Harvard University, USA), Sanjay Sane (NCBS-TIFR, India) and Vatsala Thirumalai (NCBS-TIFR,

From playlist ICTP-ICTS Winter School on Quantitative Systems Biology (ONLINE)

9 - Bayes' rule in inference - example: forgetting the denominator

This provides a complete example of how Bayes' rule can be used to conduct inference over a discrete parameter. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm Unfortun

From playlist Bayesian statistics: a comprehensive course

Martin Paulus: "Decision-Making and Computational Psychiatry: An Explanatory and Pragmatic Persp..."

Computational Psychiatry 2020 "Decision-Making and Computational Psychiatry: An Explanatory and Pragmatic Perspective" Martin Paulus - University of California, San Diego Abstract: Differentiating whether an action leads to an outcome by chance or by an underlying statistical regularity

From playlist Computational Psychiatry 2020

(ML 11.8) Bayesian decision theory

Choosing an optimal decision rule under a Bayesian model. An informal discussion of Bayes rules, generalized Bayes rules, and the complete class theorems.

From playlist Machine Learning

Statistical Rethinking - Lecture 01

The Golem of Prague / Small World and Large Worlds: Chapters 1 and 2 of 'Statistical Rethinking: A Bayesian Course with R Examples'.

From playlist Statistical Rethinking Winter 2015

Deep Gaussian Processes for Bayesian Inversion: Matt Dunlop, Courant

Uncertainty quantification (UQ) employs theoretical, numerical and computational tools to characterise uncertainty. It is increasingly becoming a relevant tool to gain better understanding of physical systems and to make better decisions under uncertainty. Realistic physical systems are us

From playlist Effective and efficient gaussian processes

Computational Principles Underlying the Learning of Sensorimotor... (Lecture 2) by Daniel Wolpert

PROGRAM ICTP-ICTS WINTER SCHOOL ON QUANTITATIVE SYSTEMS BIOLOGY (ONLINE) ORGANIZERS: Vijaykumar Krishnamurthy (ICTS-TIFR, India), Venkatesh N. Murthy (Harvard University, USA), Sharad Ramanathan (Harvard University, USA), Sanjay Sane (NCBS-TIFR, India) and Vatsala Thirumalai (NCBS-TIFR,

From playlist ICTP-ICTS Winter School on Quantitative Systems Biology (ONLINE)

18. Bayesian Statistics (cont.)

MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet In this lecture, Prof. Rigollet talked about Bayesian confidence regions and Bayesian estimation. License: Creative Commons BY-NC-SA More information at

From playlist MIT 18.650 Statistics for Applications, Fall 2016

11d Machine Learning: Bayesian Linear Regression

Lecture on Bayesian linear regression. By adopting the Bayesian approach (instead of the frequentist approach of ordinary least squares linear regression) we can account for prior information and directly model the distributions of the model parameters by updating with training data. Foll

From playlist Machine Learning

Formal verification and learning of complex systems - Professor Alessandro Abate

For slides, future Logic events and more, please visit: https://logic-data-science.github.io/?page=logic_learning Two known shortcomings of standard techniques in formal verification are the limited capability to provide system-level assertions, and the scalability to large-scale, complex

From playlist Logic and learning workshop

How the brain controls the body | The Royal Society

In his Ferrier Lecture 2021, Professor Daniel Wolpert explores the amazing processes by which the brain controls the body. The effortless ease with which we move our arms, our eyes, even our lips when we speak masks the true complexity of the control processes involved. This is evident wh

From playlist Latest talks and lectures

Bayesian inference by John Reinitz

Winter School on Quantitative Systems Biology DATE: 04 December 2017 to 22 December 2017 VENUE: Ramanujan Lecture Hall, ICTS, Bengaluru The International Centre for Theoretical Sciences (ICTS) and the Abdus Salam International Centre for Theoretical Physics (ICTP), are organizing a Wint

From playlist Winter School on Quantitative Systems Biology

8 - Bayes' rule in inference - example: the posterior distribution

This provides a complete example of how Bayes' rule can be used to conduct inference over a discrete parameter. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm Unfortun

From playlist Bayesian statistics: a comprehensive course