Tim Sullivan: Brittleness and robustness of Bayesian inference for complex systems
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist Numerical Analysis and Scientific Computing
Robust Design Discovery and Exploration in Bayesian Optimization
A Google TechTalk, presented by Ilija Bogunovic, 2022/10/04 BayesOpt Speaker Series - ABSTRACT: Whether in biological design, causal discovery, material production, or physical sciences, one often faces decisions regarding which new data to collect or which experiments to perform. There is
From playlist Google BayesOpt Speaker Series 2021-2022
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
Bayesian vs frequentist statistics
This video provides an intuitive explanation of the difference between Bayesian and classical frequentist statistics. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm Un
From playlist Bayesian statistics: a comprehensive course
Bayesian vs frequentist statistics probability - part 1
This video provides an intuitive explanation of the difference between Bayesian and classical frequentist statistics. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm Unfo
From playlist Bayesian statistics: a comprehensive course
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
(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
Stanford Seminar - Theories of inference for visual analysis
Jessica Hullman Northwestern University December 3, 2021 Research and development in computer science and statistics have produced increasingly sophisticated software interfaces for interactive and exploratory analysis, optimized for easy pattern finding and data exposure. But design p
From playlist Stanford Seminars
How to run A/B Tests as a Data Scientist!
Let's learn about how & why you should use Bayesian Testing. And some advantages of the Bayesian approach over frequentist approach with REAL data/code. Note: Bayesian Appraoch isn't necessarily better in every way - it is another perspective of looking at data. CODE: https://github.com/a
From playlist A/B Testing
DDPS | Parameter Subset Selection and Active Subspace Techniques for Engineering & Biological Models
Engineering and biological models generally have a number of parameters which are nonidentifiable in the sense that they are not uniquely determined by measured responses. Furthermore, the computational cost of high-fidelity simulation codes often precludes their direct use for Bayesian m
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Stanford Seminar - ML Explainability Part 5 I Future of Model Understanding
Professor Hima Lakkaraju discusses the many future research directions for building explainable AI including better algorithms for post hoc explanations, theoretical analysis of interpretable models and explanation methods, and empirical evaluation of the utility of model explanations. Vi
From playlist Stanford Seminars
The Monte Carlo Fusion Problem by Gareth Roberts
Program Advances in Applied Probability II (ONLINE) ORGANIZERS: Vivek S Borkar (IIT Bombay, India), Sandeep Juneja (TIFR Mumbai, India), Kavita Ramanan (Brown University, Rhode Island), Devavrat Shah (MIT, US) and Piyush Srivastava (TIFR Mumbai, India) DATE: 04 January 2021 to 08 Januar
From playlist Advances in Applied Probability II (Online)
Statistical Rethinking 2023 - 02 - The Garden of Forking Data
Slides and other course materials at https://github.com/rmcelreath/stat_rethinking_2023 Pause music: https://www.youtube.com/watch?v=_tV5LEBDs7w Outline 00:00 Introduction 02:38 Generative model 08:42 The Garden of Forking Data 23:48 Bayesian updating 31:31 Probability 36:26 Testing 44:3
From playlist Statistical Rethinking 2023
Kerrie Mengersen: Bayesian Modelling
Abstract: This tutorial will be a beginner’s introduction to Bayesian statistical modelling and analysis. Simple models and computational tools will be described, followed by a discussion about implementing these approaches in practice. A range of case studies will be presented and possibl
From playlist Probability and Statistics
Mean field asymptotics in high-dimensional statistics – A. Montanari – ICM2018
Probability and Statistics Invited Lecture 12.16 Mean field asymptotics in high-dimensional statistics: From exact results to efficient algorithms Andrea Montanari Abstract: Modern data analysis challenges require building complex statistical models with massive numbers of parameters. It
From playlist Probability and Statistics
Safety and robustness for deep learning with provable guarantees - Marta Kwiatkowska - Oxford
Computing systems are becoming ever more complex, with automated decisions increasingly often based on deep learning components. A wide variety of applications are being developed, many of them safety-critical, such as self-driving cars and medical diagnosis. Since deep learning is unstabl
From playlist Interpretability, safety, and security in AI
Sudipto Banerjee: High-dimensional Bayesian geostatistics
Abstract: With the growing capabilities of Geographic Information Systems (GIS) and user-friendly software, statisticians today routinely encounter geographically referenced data containing observations from a large number of spatial locations and time points. Over the last decade, hierarc
From playlist Probability and Statistics
Statistics in cyber-security: Dr Nick Heard, Imperial College London
Abstract: Data science techniques have an important role to play in the next generation of cyber-security defences. Inside a typical enterprise computer network, a number of high-volume data sources are available which could aid the discovery and prevention of cyber-attacks and network mi
From playlist Data-Centric Engineering Seminar Series
Bayesian Linear Regression : Data Science Concepts
The crazy link between Bayes Theorem, Linear Regression, LASSO, and Ridge! LASSO Video : https://www.youtube.com/watch?v=jbwSCwoT51M Ridge Video : https://www.youtube.com/watch?v=5asL5Eq2x0A Intro to Bayesian Stats Video : https://www.youtube.com/watch?v=-1dYY43DRMA My Patreon : https:
From playlist Bayesian Statistics