From Tetherless World Wiki
Citation: Homer L. Chin and Gregory F. Cooper. (1988) Stochastic Simulation of Casual Bayesian Models. In International Journal of Approximate Reasoning, Volume 2, ,,331,1988.
| Publication article ( Edit )
|
| type | Article
|
| bibtype | article
|
| Bibtex basics
|
| author | Homer L. Chin and Gregory F. Cooper
|
| title | Stochastic Simulation of Casual Bayesian Models
|
| journal | International Journal of Approximate Reasoning, Volume 2
|
| pages | 331
|
| year | 1988
|
| Bibtex more
|
| note | Chapter: Bayesian Belief Network Inference Using Simulation.
|
| publisher | North-Holland
|
| Access Paper
|
| abstract | This paper examines Bayesian belief network inference using simulation as a method for computing the posterior probabilities of network variables. Specifically, it examines the use of a method described by Henrion, called logic sampling, and a method described by Pearl, called stochastic simulation. We first review the conditions under which logic sampling is computationally infeasible. Such cases motivated the development of the Pearl's stochastic simulation algorithm. We have found that this stochastic simulation algorithm, when applied to certain networks, leads to much slower than expected convergence to the true posterior probabilities. This behavior is a result of the tendency for local areas in the network to become fixed through many simulation cycles. The time required to obtain significant convergence can be made arbitrarily long be strengthening the probabilistic dependency between nodes. We propose the use of several forms of graph modification, such as graph pruning, arc reversal, and node reduction, in order to convert some networks into formats that are computationally more efficient for simulation.
|
| KSL Technical Report ID: KSL-87-22
|
Facts about Stochastic Simulation of Casual Bayesian ModelsRDF feed
| Abstract | This paper examines Bayesian belief networ … This paper examines Bayesian belief network inference using simulation as a method for computing the posterior probabilities of network variables. Specifically, it examines the use of a method described by Henrion, called logic sampling, and a method described by Pearl, called stochastic simulation. We first review the conditions under which logic sampling is computationally infeasible. Such cases motivated the development of the Pearl's stochastic simulation algorithm. We have found that this stochastic simulation algorithm, when applied to certain networks, leads to much slower than expected convergence to the true posterior probabilities. This behavior is a result of the tendency for local areas in the network to become fixed through many simulation cycles. The time required to obtain significant convergence can be made arbitrarily long be strengthening the probabilistic dependency between nodes. We propose the use of several forms of graph modification, such as graph pruning, arc reversal, and node reduction, in order to convert some networks into formats that are computationally more efficient for simulation. utationally more efficient for simulation. |
| Author | Homer L. Chin and Gregory F. Cooper + |
| Bibtype | article + |
| Has author | Homer L. Chin and Gregory F. Cooper + |
| Has identifier | KSL-87-22 + |
| Has publishing details | ,,331,1988 + |
| Has title | Stochastic Simulation of Casual Bayesian Models + |
| Has where published | International Journal of Approximate Reasoning, Volume 2 + |
| Has year | 1988 + |
| Journal | International Journal of Approximate Reasoning, Volume 2 + |
| Ksl tr id | KSL-87-22 + |
| Note | Chapter: Bayesian Belief Network Inference Using Simulation. |
| Pages | 331 + |
| Process note | GOOGLE + |
| Publisher | North-Holland + |
| Title | Stochastic Simulation of Casual Bayesian Models + |
| Year | 1988 + |
