Monte Carlo Simulation for Statistical Inference, Model Selection and Decision Making
Description
The first part of his course will consist of two presentations. In the first presentation, he will introduce fundamentals of Monte Carlo simulation for statistical inference, with emphasis on algorithms such as importance sampling, particle filtering and smoothing for dynamic models, Markov chain Monte Carlo, Gibbs and Metropolis-Hastings, blocking and mixtures of MCMC kernels, Monte Carlo EM, sequential Monte Carlo for static models, auxiliary variable methods (Swedsen-Wang, hybrid Monte Carlo and slice sampling), and adaptive MCMC. The algorithms will be illustrated with several examples: image tracking, robotics, image annotation, probabilistic graphical models, and music analysis. The second presentation will target model selection and decision making problems. He will describe the reversible-jump MCMC algorithm and illustrate it with application to simple mixture models and nonlinear regression with an unknown number of basis functions. He will show how to apply this algorithm to general Markov decision processes (MDPs). The course will also cover other Monte Carlo simulation methods for partially observed Markov decision processes (POMDPs) using policy gradients, common random number generation, and active exploration with Gaussian processes. An outline to some applications of these methods to robotics and the design of computer game architectures will be given. The presentation will end with the problem of Monte Carlo simulation for Bayesian nonlinear experimental design, with application to financial modeling, robot exploration, drug treatments, dynamic sensor networks, optimal measurement and active vision.
| Slides | |
| 0:00 | Monte Carlo Methods in Learning |
| 5:59 | Overview |
| 7:44 | The idea (1) |
| 8:57 | The idea (2) |
| 9:08 | The idea (3) |
| 11:37 | The idea (4) |
| 12:20 | History of the Monte Carlo method |
| 13:06 | History of the Monte Carlo method: The bomb and ENIAC |
| 15:26 | History of the Monte Carlo method |
| 16:04 | Applications of Monte Carlo |
| 18:19 | A Simulation Example |
| 23:11 | Learning and Bayesian inference |
| 50:00 | Integrals in Probabilistic Inference |
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Good presentation. The cameraman is sleepy
Excellent... Prof.'s Examples are really useful and make us easy to apply in real time
Where are the other 3 lectures?
There are altogether 6 videos in this lecture. Only three of them have been sent directly from the event. The other three will be published once we get the tapes from Australia - probably within a week or so.
Very good lecture. Thank You. I'm also interested in the other 3 lecutres, but they are not published yet app. one month later. Is there still an chance to get them ?
Excellent teacher!
The lecture from Nando de Freitas is now complete.
Awesome cameraman! He worked very hard to always be in the lecture theater and delivered us good quality video lectures which will not be possible without the joint effort of http://videolectures.net team. Thanks so much for the great work, guys!