Name
Abstract | ||
|---|---|---|
An estimation algorithm for selection probabilities of probabilistic Boolean networks is developed in this paper. A recursive estimation procedure is deduced from Bayes theorem and gives the expression of posterior probability distribution of the selection probabilities. To realize the distributions in the fashion of numerical computation, Markov chain Monte Carlo (MCMC) method with Metropolis-Hastings sampler is exploited and provides approximated probability densities. Some numerical examples are illustrated to demonstrate the effectiveness and computation complexity of the proposed framework. |
Year | Venue | Field |
|---|---|---|
2019 | 2019 12TH ASIAN CONTROL CONFERENCE (ASCC) | Markov chain Monte Carlo,Probability estimation,Computer science,Algorithm,Posterior probability,Probabilistic logic,Recursion,Computation complexity,Bayes' theorem,Computation |
DocType | ISSN | Citations |
Conference | 2072-5639 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mitsuru Toyoda | 1 | 0 | 0.34 |
| Yuhu Wu | 2 | 5 | 2.10 |