方法证据记录
Bayesian Cellular Automata
Bayesian Cellular Automata (BCA) couples the local-rule spatial dynamics of classical cellular automata with Bayesian inference to learn or calibrate transition probabilities from observed data. Rather than fixing rules by hand, the analyst encodes prior knowledge about how cells change state and updates those beliefs with empirical evidence, producing a posterior distribution over rule parameters that drives principled uncertainty-aware simulation.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Bayesian Cellular Automata — Probabilistic calibration of transition rules via Bayesian inference
分类方法记录 · process-pipeline / simulation
- Hosseinali, F., Alesheikh, A. A., Nourian, F. (2013). Agent-based modeling of urban land-use development, case study: Simulating future scenarios of Qazvin city. Cities, 31, 105-113. · DOI 10.1016/j.cities.2012.09.002
- Cellular automaton. Wikipedia. · URL
精选声明
声明已持久化到证据分类账中，每个声明都有自己的评估。
尚无精选声明
当分类账中没有声明时，此视图不会自行创建声明评估。
相关方法
从方法图中生成，显示为机器建议的关系 — 不推断任何证据声明。