Bayesian estimation of templates in computational anatomy

(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

(ML 12.4) Bayesian model selection

Approaches to model selection from a Bayesian perspective: Bayesian model averaging (BMA), "Type II MAP", and Type II Maximum Likelihood (a.k.a. ML-II, a.k.a. the evidence approximation, a.k.a. empirical Bayes).

From playlist Machine Learning

(ML 13.6) Graphical model for Bayesian linear regression

As an example, we write down the graphical model for Bayesian linear regression. We introduce the "plate notation", and the convention of shading random variables which are being conditioned on.

From playlist Machine Learning

Maximum Likelihood Estimation and Bayesian Estimation

http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Introduces the maximum likelihood and Bayesian approaches to finding estimators of parameters.

From playlist Estimation and Detection Theory

(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

Marcelo Pereyra: Bayesian inference and mathematical imaging - Lecture 1: Bayesian analysis...

Bayesian inference and mathematical imaging - Part 1: Bayesian analysis and decision theory Abstract: This course presents an overview of modern Bayesian strategies for solving imaging inverse problems. We will start by introducing the Bayesian statistical decision theory framework underp

From playlist Probability and Statistics

(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

(ML 10.7) Predictive distribution for linear regression (part 4)

How to compute the (posterior) predictive distribution for a new point, under a Bayesian model for linear regression.

From playlist Machine Learning

Robert E. Kass - Statistical Assessment of Interaction Among Brain Regions...

Statistical Assessment of Interaction Among Brain Regions from Multi-Electrode Recordings ---------------------------------- Institut Henri Poincaré, 11 rue Pierre et Marie Curie, 75005 PARIS http://www.ihp.fr/ Rejoingez les réseaux sociaux de l'IHP pour être au courant de nos actualités

From playlist Workshop "Workshop on Mathematical Modeling and Statistical Analysis in Neuroscience" - January 31st - February 4th, 2022

Hierarchical modelling of weak lensing and photometric (...) - Heavens - Workshop 2 - CEB T3 2018

Heavens (Imperial College) / 22.10.2018 Hierarchical modelling of weak lensing and photometric redshifts ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincare/ Twitter

From playlist 2018 - T3 - Analytics, Inference, and Computation in Cosmology

Mixed-effect model for the spatiotemporal analysis of longitudinal (...) - Workshop 2 - CEB T1 2019

Stéphanie Allassonnière (Univ. Paris Descartes) / 13.03.2019 Mixed-effect model for the spatiotemporal analysis of longitudinal manifold-valued data. In this talk, I propose to present a generic hierarchical spatiotemporal model for longitudinal manifold-valued data, which consists in r

From playlist 2019 - T1 - The Mathematics of Imaging

Ender Konukoglu: "On Bayesian models with networks for reconstruction and detection"

Deep Learning and Medical Applications 2020 "On Bayesian models with networks for reconstruction and detection" Ender Konukoglu, ETH Zurich Abstract: Neural networks have demonstrated tremendous potential for medical image analysis. In this talk, I will focus on utilizing these models in

From playlist Deep Learning and Medical Applications 2020

Astrophysical Relativity @ICTS by Haris M K

ICTS In-house 2019 Organizers: Adhip Agarwala, Ganga Prasath, Rahul Kashyap, Gayathri Raman, Priyanka Maity Date and Time: 23rd April, 2019 Venue: Ramanujan Lecture Hall, ICTS Bangalore inhouse@icts.res.in An exclusive day to exchange ideas and discuss research amongst members of ICTS.

From playlist ICTS In-house 2019

Tyson Littenberg - Building flexible, but not too flexible, models of gravitational wave data

Recorded 15 November 2021. Tyson Littenberg of the NASA - Marshall Space Flight Center presents "Building flexible, but not too flexible, models of gravitational wave data" at IPAM's Workshop III: Source inference and parameter estimation in Gravitational Wave Astronomy. Abstract: Gravitat

From playlist Workshop: Source inference and parameter estimation in Gravitational Wave Astronomy

Marcelo Pereyra: Bayesian inference and mathematical imaging - Lecture 4: mixture...

Bayesian inference and mathematical imaging - Part 4: mixture, random fields and hierarchical models Abstract: This course presents an overview of modern Bayesian strategies for solving imaging inverse problems. We will start by introducing the Bayesian statistical decision theory framewo

From playlist Probability and Statistics

A Blueprint of Standardized and Composable Machine Learning - Eric Xing

Seminar on Theoretical Machine Learning Topic: A Blueprint of Standardized and Composable Machine Learning Speaker: Eric Xing Affiliation: Carnegie Mellon University Date: August 6, 2020 For more video please visit http://video.ias.edu

From playlist Mathematics

Statistical Rethinking - Lecture 19

Lecture 19 - Gaussian processes, measurement error - Statistical Rethinking: A Bayesian Course with R Examples

From playlist Statistical Rethinking Winter 2015

Bayesian data interpretation with large scale cosmological (...) - Jasche - Workshop 2 - CEB T3 2018

Jens Jasche (Stockholm University) / 25.10.2018 Bayesian data interpretation with large scale cosmological models ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincare/

From playlist 2018 - T3 - Analytics, Inference, and Computation in Cosmology

(ML 10.6) Predictive distribution for linear regression (part 3)

How to compute the (posterior) predictive distribution for a new point, under a Bayesian model for linear regression.

From playlist Machine Learning

Challenges in Source Parameter Estimation in GW Astronomy by Rajesh Nayak

20 March 2017 to 25 March 2017 VENUE: Madhava Lecture Hall, ICTS, Bengaluru This joint program is co-sponsored by ICTS and SAMSI (as part of the SAMSI yearlong program on Astronomy; ASTRO). The primary goal of this program is to further enrich the international collaboration in the area

From playlist Time Series Analysis for Synoptic Surveys and Gravitational Wave Astronomy