Learning and Inference in Computational and Systems Biology

TBA

author:Andrew Golightly, School of Mathematics and Statistics, Newcastle University
published: April 16, 2009, recorded: April 2009, views: 19

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Slides

Slides
0:00	Hybrid Inference for Stochastic Kinetic Models
0:56	Overview
1:40	Modelling
3:14	Modelling cont’d
4:16	Inference for the Exact Model - 1
5:13	Inference for the Exact Model - 2
5:48	Inference for the Exact Model - 3
6:52	Inference for the Exact Model cont’d - 1
7:09	Inference for the Exact Model cont’d - 2
7:44	Inference for the Exact Model cont’d - 3
8:04	Inference for the Exact Model - 3
8:15	Difficulties - 1
9:12	Difficulties - 2
10:48	Diffusion Approximation - 1
11:51	Diffusion Approximation - 2
12:55	Diffusion Approximation - 3
14:15	Diffusion Approximation – Inference - 1
14:54	Diffusion Approximation – Inference - 2
15:29	Diffusion Approximation – Inference
15:45	Difficulties - 1
16:07	Difficulties - 2
16:25	Difficulties - 3
16:58	Diffusion Approximation – Inference - 2
17:33	Hybrid Inference – Motivation - 1
18:06	Hybrid Inference – Motivation - 2
19:02	Hybrid Inference – Motivation - 3
20:01	Hybrid Inference – Motivation - 4
20:04	Hybrid Inference – Motivation - 5
20:37	Hybrid Simulation
22:43	Hybrid Simulation cont’d
23:49	Performance of the Hybrid Simulator
25:15	Bayesian Filtering - 1
26:04	Bayesian Filtering - 2
27:17	Bayesian Filtering - 3
28:07	A Particle Approach - 1
28:47	Bayesian Filtering - 3
29:05	A Particle Approach - 1
29:10	A Particle Approach - 2
30:54	Toy Application Revisited
31:36	Results – 20,000 particles - 1
32:11	Results – 20,000 particles - 2
32:48	Summary
34:52	- Questions
36:24	- Questions

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