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» Causal inference using the algorithmic Markov condition
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UAI
2008
14 years 11 months ago
Causal discovery of linear acyclic models with arbitrary distributions
An important task in data analysis is the discovery of causal relationships between observed variables. For continuous-valued data, linear acyclic causal models are commonly used ...
Patrik O. Hoyer, Aapo Hyvärinen, Richard Sche...
AAAI
2008
15 years 2 days ago
Exploiting Causal Independence Using Weighted Model Counting
Previous studies have demonstrated that encoding a Bayesian network into a SAT-CNF formula and then performing weighted model counting using a backtracking search algorithm can be...
Wei Li 0002, Pascal Poupart, Peter van Beek
AAAI
2006
14 years 11 months ago
Identification of Joint Interventional Distributions in Recursive Semi-Markovian Causal Models
This paper is concerned with estimating the effects of actions from causal assumptions, represented concisely as a directed graph, and statistical knowledge, given as a probabilit...
Ilya Shpitser, Judea Pearl
PAMI
2010
238views more  PAMI 2010»
14 years 8 months ago
Tracking Motion, Deformation, and Texture Using Conditionally Gaussian Processes
—We present a generative model and inference algorithm for 3D nonrigid object tracking. The model, which we call G-flow, enables the joint inference of 3D position, orientation, ...
Tim K. Marks, John R. Hershey, Javier R. Movellan
ICGI
2004
Springer
15 years 3 months ago
Navigation Pattern Discovery Using Grammatical Inference
We present a method for modeling user navigation on a web site using grammatical inference of stochastic regular grammars. With this method we achieve better models than the previo...
Nikolaos Karampatziakis, Georgios Paliouras, Dimit...