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Estimating parameters and hidden states in biological networks with particle filters
Published on 2007-09-076097 Views
Abstract. Identifying biological networks requires to develop models able to capture their dynamics and statistical learning methods to estimate their parameters from time-series measurements. In part
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Presentation
Estimating Parameters and Hidden States in Biological Networks with Particle Filters00:00
Outline00:21
Outline - Problem00:43
Problem: Reverse Engineering of Biological Networks00:45
Outline - Filtering in State Space Models02:15
Nonlinear State-Space Model - part 102:22
Nonlinear State-Space Model - part 202:28
Bayesian estimation: Filtering03:15
Recursive Filtering Algorithm - part 103:56
Recursive Filtering Algorithm - part 204:01
Recursive Filtering Algorithm - part 304:36
Outline - Particle Filter05:39
Sequential Monte Carlo Methods or Particle filters05:41
Importance sampling06:19
Sequential Importance Sampling with Resampling06:56
Sampling-importance Resampling: Bootstrap Filter [Gordon 1993]08:23
Theoretical Convergence09:11
Problem with Bootstrap Filter - part 109:41
Problem with Bootstrap Filter - part 210:25
Outline - Results11:12
Repressilator11:13
Result for the Repressilator11:51
Repressilator - part 112:33
Repressilator - part 212:39
Repressilator - part 313:05
Repressilator - part 413:36
Conclusion and Future work14:27