Bayesian Dynamic Modeling: Sharing Information Across Time and Space

Автор: UW Video

Загружено: 2013-11-13

Просмотров: 26686

Описание: This talk will highlight some of the benefits and challenges associated with harnessing the temporal structure present in many datasets. The focus is on Bayesian dynamic modeling approaches, and in particular, the idea of sharing information across time and "space," where space generically refers to the dimensions of the time series. Emily Fox, UW Assistant Professor of Statistics, discusses how to exploit nonparametric and hierarchical models to capture repeated patterns in time and similar structure in space, enabling the modeling of complex and high-dimensional time series. Applications of such approaches are quite diverse, and she demonstrate this by touching upon work in the tasks of speaker diarization, analyzing human motion, detecting changes in volatility of stock indices, parsing EEG, word classification from MEG, and predicting rates of violent crimes in DC and influenza rates in the US.

Не удается загрузить Youtube-плеер. Проверьте блокировку Youtube в вашей сети.
Повторяем попытку...

Bayesian Dynamic Modeling: Sharing Information Across Time and Space

Доступные форматы для скачивания:

Скачать видео

Информация по загрузке:

Скачать аудио

Похожие видео

Tamara Broderick: Variational Bayes and Beyond: Bayesian Inference for Big Data (ICML 2018 tutorial)

Tamara Broderick: Variational Bayes and Beyond: Bayesian Inference for Big Data (ICML 2018 tutorial)

Байесовские иерархические модели

Байесовские иерархические модели

The Anatomy of a Dynamical System

The Anatomy of a Dynamical System

The Search for Randomness with Persi Diaconis

The Search for Randomness with Persi Diaconis

Anima Anandkumar - Neural operator: A new paradigm for learning PDEs

Anima Anandkumar - Neural operator: A new paradigm for learning PDEs

Nonparametric Bayesian Methods: Models, Algorithms, and Applications I

Nonparametric Bayesian Methods: Models, Algorithms, and Applications I

Теорема Байеса, геометрия изменения убеждений

Теорема Байеса, геометрия изменения убеждений

Professor Mike West: Structured Dynamic Graphical Models & Scaling Multivariate Time Series

Professor Mike West: Structured Dynamic Graphical Models & Scaling Multivariate Time Series

Efficient Bayesian inference with Hamiltonian Monte Carlo -- Michael Betancourt (Part 1)

Efficient Bayesian inference with Hamiltonian Monte Carlo -- Michael Betancourt (Part 1)

History of Bayesian Neural Networks (Keynote talk)

History of Bayesian Neural Networks (Keynote talk)

Hierarchical Modelling in Stan: Predicting the Premier League

Hierarchical Modelling in Stan: Predicting the Premier League

Dynamic Bayesian Network Inferencing for Nonhomogenous Complex Systems

Dynamic Bayesian Network Inferencing for Nonhomogenous Complex Systems

The R-INLA project: Overview and recent developments

The R-INLA project: Overview and recent developments

All About that Bayes: Probability, Statistics, and the Quest to Quantify Uncertainty

All About that Bayes: Probability, Statistics, and the Quest to Quantify Uncertainty

Introduction to Bayesian data analysis - part 1: What is Bayes?

Introduction to Bayesian data analysis - part 1: What is Bayes?

Machine Learning Work Shop - Bayesian Nonparametrics for Complex Dynamical Phenomena

Machine Learning Work Shop - Bayesian Nonparametrics for Complex Dynamical Phenomena

Andrew Gelman: Introduction to Bayesian Data Analysis and Stan with Andrew Gelman

Andrew Gelman: Introduction to Bayesian Data Analysis and Stan with Andrew Gelman

Bayesian Statistics without Frequentist Language

Bayesian Statistics without Frequentist Language

Two Effective Algorithms for Time Series Forecasting

Two Effective Algorithms for Time Series Forecasting

Frequentism and Bayesianism: What's the Big Deal? | SciPy 2014 | Jake VanderPlas

Frequentism and Bayesianism: What's the Big Deal? | SciPy 2014 | Jake VanderPlas