Course : Vision as Bayesian Inference . Lecture

Course Notes for Advanced Probabilistic Machine Learning

These are lecture notes for the seminar ELEN E9801 Topics in Signal Processing: “Advanced Probabilistic Machine Learning” taught at Columbia University in Fall 2014. They are transcribed almost verbatim from the handwritten lecture notes, and so they preserve the original bulleted structure and are light on the exposition. Some lectures therefore also have a certain amount of reiterating in the...

Bayesian approach to inference of population structure

Methods of inferring the population structure‎, ‎its applications in identifying disease models as well as foresighting the physical and mental situation of human beings have been finding ever-increasing importance‎. ‎In this article‎, ‎first‎, ‎motivation and significance of studying the problem of population structure is explained‎. ‎In the next section‎, ‎the applications of inference of p...

Lecture 22: Continued Introduction to Bayesian Inference and Use of the MCMC Algorithm for Inference

Bayesian Inference in Model-Based Machine Vision

This is a preliminary version of visual interpretation integrating multiple sensors in SUCCESSOR, an intelligent, model-based vision system. We pursue a thorough integration of hierarchical Bayesian inference with comprehensive physical representation of objects and their relations in a system for reasoning with geometry, surface materials and sensor models in machine vision. Bayesian inference...

A web-based bayesian intelligent tutoring system for computer programming

In this paper, we present a Web-based intelligent tutoring system, called BITS. The decision making process conducted in our intelligent system is guided by a Bayesian network approach to support students in learning computer programming. Our system takes full advantage of Bayesian networks, which are a formal framework for uncertainty management in Artificial Intelligence based on probability ...