Graphs for Margins of Bayesian Networks
نویسندگان
چکیده
منابع مشابه
Explaining Legal Bayesian Networks Using Support Graphs
Legal reasoning about evidence can be a precarious exercise, in particular when statistics are involved. A number of recent miscarriages of justice have provoked a scientific interest in formal models of legal evidence. Two such models are presented by Bayesian networks (BNs) and argumentation. A limitation of argumentation is that it is difficult to embed probabilities. BNs, on the other hand,...
متن کاملLearning Bayesian Networks with Largest Chain Graphs
This paper proposes a new approach for designing learning bayesian network algorithms that explore the structure equivalence classes space. Its main originality consists in the representation of equivalence classes by largest chain graphs, instead of essential graphs which are generally used in the similar task. We show that this approach drastically simplifies the algorithms formulation and ha...
متن کاملConditioning Graphs: Practical Structures for Inference in Bayesian Networks
Probability is a useful tool for reasoning when faced with uncertainty. Bayesian networks offer a compact representation of a probabilistic problem, exploiting independence amongst variables that allows a factorization of the joint probability into much smaller local probability distributions. The standard approach to probabilistic inference in Bayesian networks is to compile the graph into a j...
متن کاملBayesian Networks Factor Graphs the Case-Factor Algorithm and the Junction Tree Algorithm 1 Bayesian Networks
We will use capital letters for random variables and lower case letters for values of those variables. A Bayesian network is a triple 〈V,G,P〉 where V is a set of random variables X1, . . ., Xn, G is a directed acyclic graph (DAG) whose nodes are the variables in V , and P is a set of conditional probability tables as described below. The conditional probability tables determine a probability di...
متن کاملBayesian Networks from the Point of View of Chain Graphs
The paper gives a few arguments in favour of use of chain graphs for description of proba bilistic conditional independence structures. Every Bayesian network model can be equiva lently introduced by means of a factorization formula with respect to chain graph which is Markov equivalent to the Bayesian network. A graphical characterization of such graphs is given. The class of equivalent grap...
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: Scandinavian Journal of Statistics
سال: 2015
ISSN: 0303-6898
DOI: 10.1111/sjos.12194