Imprecise Bayesian Networks as Causal Models
نویسندگان
چکیده
منابع مشابه
Hierarchical Bayesian models as formal models of causal reasoning
knowledge can come in various forms, and it may form complex hierarchies. The most abstract form of knowledge may be called causal principles, which probably include the assumption that causes precede their effects, that nothing happens without a cause, and that causes generate their effect unless prevented by an inhibitory factor (Audi 1995). A fundamental assumption might also be that a manip...
متن کاملPropagating Imprecise Probabilities in Bayesian Networks
Often experts are incapable of providingèxact' probabilities; likewise, samples on which the probabilities in networks are based must often be small and preliminary. In such cases the probabilities in the networks are imprecise. The imprecision can be handled by second-order probability distributions. It is convenient to use beta or Dirichlet distributions to express the uncertainty about proba...
متن کاملCausal Interaction in Bayesian Networks
Artificial Intelligence (AI) and Philosophy of Science share a fundamental problem—that of understanding causality. Bayesian network techniques have recently been used by Judea Pearl in a new approach to understanding causality and causal processes (Pearl, 2000). Pearl’s approach has great promise, but needs to be supplemented with an explicit account of causal interaction. Thus far, despite co...
متن کاملCausal reversibility in Bayesian networks
Causal manipulation theorems proposed by Spirtes et al. and Pearl in the context of directed probabilistic graphs, such as Bayesian networks, oŒer a simple and theoretically sound formalism for predicting the eŒect of manipulation of a system from its causal model. While the theorems are applicable to a wide variety of equilibrium causal models, they do not address the issue of reversible causa...
متن کاملA software system for causal reasoning in causal Bayesian networks
Knowing the cause and effect is important to researchers who are interested in modeling the effects of actions, and Artificial Intelligence researchers are among them. One commonly used method for modeling cause and effect is graphical model. Bayesian Network is a probabilistic graphical model for representing and reasoning uncertain knowledge. It has been used as a fundamental tool and is beco...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Information
سال: 2018
ISSN: 2078-2489
DOI: 10.3390/info9090211