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Estimating parameters and hidden states in biological networks with particle filters
Published on Sep 07, 20076092 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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Chapter list
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