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| Bayesiläinen verkko× | Markov-ketju-Monte Carlo (MCMC)× | |
|---|---|---|
| Tieteenala | Bayesilainen tilastotiede | Bayesilainen tilastotiede |
| Menetelmäperhe | Bayesian methods | Bayesian methods |
| Syntyvuosi≠ | 1988 | — |
| Kehittäjä≠ | Judea Pearl | — |
| Tyyppi≠ | Probabilistic graphical model | Posterior sampling algorithm |
| Alkuperäislähde≠ | Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797 | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 |
| Rinnakkaisnimet≠ | Bayes network, belief network, probabilistic graphical model, directed graphical model | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Liittyvät≠ | 4 | 3 |
| Tiivistelmä≠ | A Bayesian network is a probabilistic graphical model, introduced by Judea Pearl in 1988, that encodes a set of variables and their conditional dependencies as a directed acyclic graph (DAG). Each node represents a variable; each directed edge encodes a direct probabilistic influence. By combining Bayes' rule with the graph's conditional independence structure, the model supports reasoning under uncertainty — computing the probability of any variable given observed evidence about others. | Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model. |
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