PPT-Uncertainty and the Bayesian Brain

sources sensoryprocessing noise ignorance change consequences inference learning coding distributionalprobabilistic population codes neuromodulators Multisensory Integration   apply the previous analysis. De64257nition A Bayesian nonparametric model is a Bayesian model on an in64257nitedimensional parameter space The parameter space is typically chosen as the set of all possi ble solutions for a given learning problem For example in a regression prob P(. A . &. B. ) . = . P(. A. |. B. ) * P(. B. ). Product Rule:. Bayesian Reasoning. P(. A . &. B. ) . = . P(. A. |. B. ) * P(. B. ). Product Rule:. Shorthand for . . P(A=true & B=true) = P(A=true | B=true) * P(B=true). BigData. Jay Gu. Feb 7 2013. MapReduce. Homework 1 Review. Logistic Regression. Linear separable case, how many solutions?. Suppose . wx. = 0 is the decision boundary,. (a * w)x = 0 will have the same boundary, but more compact level set.. Author: David Heckerman. . Presented By:. Yan Zhang - 2006. Jeremy Gould – 2013. 1. Outline. Bayesian Approach. Bayesian vs. classical probability methods. Examples. Bayesian Network. Structure. Why do we work on Computational Biology?. Slides will be available on . http://www.dcs.warwick.ac.uk/~feng/combio.html. Computational Biology in Practice . Introduction and model fitting. Frequency . Stanford University, USA. A strategy for managing uncertainty. Importance of uncertainty and risk. New well planned. P1. P2. P3. P4. West-Coast Africa (WCA) slope-valley system. Data courtesy of Chevron. Problem Formulation. Goal. Discover the X that maximizes Y. Global optimization. Active experimentation. We can choose which values of X we wish to evaluate. When is Bayesian optimization particularly useful?. “The . Human Factor in . Risk”. London. 8–9 . September 2012. The Philosophy of . Risk. Martin . Sewell. Risk. In my (Bayesian) mind, . risk. . is . uncertainty.. U. ncertainty. . is best described using a probability distribution, and the broader the distribution, the greater the uncertainty. April 5-8, 2016. Lausanne, Switzerland. Towards Uncertainty Quantification in 21st Century Sea-Level Rise Predictions: Efficient Methods for . Bayesian Calibration and Forward Propagation of Uncertainty for Land-Ice . Dave LeBlond. MBSW-38. May 19, 2015. . 1. Acknowledgements. 2. Thanks to:. Stan . Altan. (J&J. ). Bill Porter (PPP LLC). Yan Shen (J&J. ). Jyh-Ming Shoung (J&J). for organizing this session, inviting me, inspiring discussions, and providing the 3 Gauge R&R examples.. April 5-8, 2016. Lausanne, Switzerland. Towards Uncertainty Quantification in 21st Century Sea-Level Rise Predictions: Efficient Methods for . Bayesian Calibration and Forward Propagation of Uncertainty for Land-Ice . Currently at Universit . Georg Schnabel. Nuclear Data Section. Division of Physical and Chemical Sciences NAPC. Department for Nuclear Sciences and Applications. IAEA, Vienna . CM on ML for ND. 11 December 2020. Outline. Jingjing Ye, PhD. BeiGene. PSI Journal Club: Bayesian Methods. Nov. 17, 2020. Outline. Background . Using a case study to illustrate potential useful Bayesian analysis. Analysis and monitoring. Design study.