Zobrazeno 1 - 10
of 283
pro vyhledávání: '"Dechter, Rina"'
This paper focuses on the computational complexity of computing empirical plug-in estimates for causal effect queries. Given a causal graph and observational data, any identifiable causal query can be estimated from an expression over the observed va
Externí odkaz:
http://arxiv.org/abs/2411.10008
The standard approach to answering an identifiable causal-effect query (e.g., $P(Y|do(X)$) when given a causal diagram and observational data is to first generate an estimand, or probabilistic expression over the observable variables, which is then e
Externí odkaz:
http://arxiv.org/abs/2408.14101
Publikováno v:
PMLR Volume 216: Uncertainty in Artificial Intelligence, 31-4 August 2023, pg. 1662--1672, Pittsburgh, PA, USA
Scientific computing has experienced a surge empowered by advancements in technologies such as neural networks. However, certain important tasks are less amenable to these technologies, benefiting from innovations to traditional inference schemes. On
Externí odkaz:
http://arxiv.org/abs/2309.00408
Publikováno v:
Constraints (2018) 23: 1
Discrete optimization is a central problem in artificial intelligence. The optimization of the aggregated cost of a network of cost functions arises in a variety of problems including (W)CSP, DCOP, as well as optimization in stochastic variants such
Externí odkaz:
http://arxiv.org/abs/1608.05288
We present SGDPLL(T), an algorithm that solves (among many other problems) probabilistic inference modulo theories, that is, inference problems over probabilistic models defined via a logic theory provided as a parameter (currently, propositional, eq
Externí odkaz:
http://arxiv.org/abs/1605.08367
Publikováno v:
Journal Of Artificial Intelligence Research, Volume 39, pages 335-371, 2010
The paper presents a scheme for computing lower and upper bounds on the posterior marginals in Bayesian networks with discrete variables. Its power lies in its ability to use any available scheme that bounds the probability of evidence or posterior m
Externí odkaz:
http://arxiv.org/abs/1401.3833
Publikováno v:
Journal Of Artificial Intelligence Research, Volume 37, pages 279-328, 2010
The paper investigates parameterized approximate message-passing schemes that are based on bounded inference and are inspired by Pearl's belief propagation algorithm (BP). We start with the bounded inference mini-clustering algorithm and then move to
Externí odkaz:
http://arxiv.org/abs/1401.3489
Publikováno v:
Journal Of Artificial Intelligence Research, Volume 33, pages 465-519, 2008
Inspired by the recently introduced framework of AND/OR search spaces for graphical models, we propose to augment Multi-Valued Decision Diagrams (MDD) with AND nodes, in order to capture function decomposition structure and to extend these compiled d
Externí odkaz:
http://arxiv.org/abs/1401.3448
Autor:
Pearl, Judea, Dechter, Rina
We show that the d -separation criterion constitutes a valid test for conditional independence relationships that are induced by feedback systems involving discrete variables.
Comment: Appears in Proceedings of the Twelfth Conference on Uncertai
Comment: Appears in Proceedings of the Twelfth Conference on Uncertai
Externí odkaz:
http://arxiv.org/abs/1302.3595