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System Identification of Enzymatic Control Processes Using Population Monte Carlo Methods
Published on Apr 04, 20076955 Views
We demonstrate the superiority of Population Monte Carlo techniques over standard Metropolis Markov Chain Monte Carlo (MCMC) methods for inferring optimal parameters for a particular mechanistic model
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Chapter list
System Identification of Enzymatic Control Processes Using Population Monte Carlo Methods00:01
Outline00:35
Biological Motivation01:49
Circadian Rhythms02:45
A Minimal Model to Describe Circadian Control03:48
An Extended Goodwin Model04:49
Example of Possible Outputs05:56
Long Term Goals06:10
Bayesian Model Inference07:07
Model Inference07:17
Defining the Model and the Data08:04
Defining a Likelihood Function08:41
A Standard MCMC Approach09:17
A Random Walk in Parameter Space09:33
Engineering a Solution10:12
Implementation10:58
Results - 15 Parallel Chains12:45
Results - 30 Parallel Chains13:25
A Population Monte Carlo Approach14:06
Comparison of Population Monte Carlo14:31
How Non-Markovian Population Monte Carlo Works15:43
How Non-Markovian Population Monte Carlo Works16:03
Features of Non-Markovian Population Monte Carlo16:50
Implementation16:54
Results for a Population of 40 Particles17:08
Potential Pitfalls17:28
Summary of Non-Markovian Population Monte Ca18:05