The home page for the "Quantifying Uncertainty" #OpenCourseWare course from 02012. No problem sets, no real lecture notes, no recorded lectures.
on 02023-10-01#OpenCourseWare #PDF slides about #particle-filters, sequential importance sampling, and #MCMC. This is from a different course, not the 02014 6.438 Algorithms for Inference, but 12.S990, “Quantifying Uncertainty”. Though it’s falsely billed as “lecture notes” it’s really PowerPoint thinking, with sequences of bullet points.
on 02023-10-01#PDF lecture notes from #OpenCourseWare “Algorithms for Inference” covering, among other things, #particle-filters. There are unfortunately some ESL solecisms in the text.
on 02023-10-01#OpenCourseWare 6.438 "Algorithms for Inference" #syllabus from 02014: “This is a graduate-level introduction to the principles of statistical inference with probabilistic models defined using graphical representations. The material in this course constitutes a common foundation for work in machine learning, signal processing, artificial intelligence, computer vision, control, and communication. (...) It is worth stressing that 6.438 Algorithms for Inference is an introductory graduate subject: it is not an advanced graduate subject for students who have already have a mastery of statistical inference algorithms, yet want to understand such material at an even more sophisticated level.”
on 02023-10-01#PDF of #problem-set 8 from MIT #OpenCourseWare “Algorithms for Inference” course, which includes a four-part #particle-filter problem
on 02023-10-01