'Posterior density' presentation slideshows

TRACKING Particle Filter

1.4k views • 136 slides

Introduction to Intensive Longitudinal Methods

1.03k views • 80 slides

710 views • 69 slides

Some Developments of ABC

Some Developments of ABC. David Balding John Molitor David Welch Imperial College London. Canonical ABC (rejection) Algorithm. Simulate parameter value ? * from prior Simulate dataset X* under the model with ? = ? *

518 views • 44 slides

Mobile Robot Localization by using Particle Filter

Mobile Robot Localization by using Particle Filter. Guanghui Luo , Chong Wang and Chong Fu Instructor: Dr. Dan Wu. Outline. Introduction Experiment platform and setup Hardware Boundary Area Setup Programming Environment Implementation Details Particle Filter

678 views • 41 slides

Bayesian Experimental Design with Stochastic Epidemic Models Gavin J Gibson & Alex Cook

Bayesian Experimental Design with Stochastic Epidemic Models Gavin J Gibson & Alex Cook School of Mathematical & Computer Sciences and Maxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh, UK Chris Gilligan Department of Plant Sciences, Cambridge University, UK

444 views • 30 slides

Bayesian Experimental Design with Stochastic Epidemic Models Gavin J Gibson & Alex Cook

Bayesian Experimental Design with Stochastic Epidemic Models Gavin J Gibson & Alex Cook School of Mathematical & Computer Sciences and Maxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh, UK Chris Gilligan Department of Plant Sciences, Cambridge University, UK

424 views • 30 slides

Bayesian Experimental Design with Stochastic Epidemic Models Gavin J Gibson & Alex Cook

Bayesian Experimental Design with Stochastic Epidemic Models Gavin J Gibson & Alex Cook School of Mathematical & Computer Sciences and Maxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh, UK Chris Gilligan Department of Plant Sciences, Cambridge University, UK

305 views • 30 slides

263 views • 25 slides

Idea of MCMC:

Remember that our objective is for some density f( y | ? ) for observations where y and ? are vectors of data and parameters, ? being sampled from a prior ?(?) then the posterior is.

202 views • 11 slides

3.7 Example: Bioassay experiment

3.7 Example: Bioassay experiment. Problem statement Observations: At each level of dose, 5 animals are tested, and number of death are observed. As dose x is increased, # of death increases.

263 views • 10 slides