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» Causal inference using the algorithmic Markov condition
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NIPS
2004
14 years 11 months ago
Learning first-order Markov models for control
First-order Markov models have been successfully applied to many problems, for example in modeling sequential data using Markov chains, and modeling control problems using the Mar...
Pieter Abbeel, Andrew Y. Ng
DAGM
2008
Springer
14 years 11 months ago
MAP-Inference for Highly-Connected Graphs with DC-Programming
The design of inference algorithms for discrete-valued Markov Random Fields constitutes an ongoing research topic in computer vision. Large state-spaces, none-submodular energy-fun...
Jörg H. Kappes, Christoph Schnörr
SOSP
2003
ACM
15 years 6 months ago
Performance debugging for distributed systems of black boxes
Many interesting large-scale systems are distributed systems of multiple communicating components. Such systems can be very hard to debug, especially when they exhibit poor perfor...
Marcos Kawazoe Aguilera, Jeffrey C. Mogul, Janet L...
ICIP
2002
IEEE
15 years 11 months ago
A Bayesian approach to inferring vascular tree structure from 2D imagery
We describe a method for inferring tree-like vascular structures from 2D imagery. A Markov Chain Monte Carlo (MCMC) algorithm is employed to produce approximate samples from the p...
Abhir Bhalerao, Elke Thönnes, Roland Wilson, ...
CIMAGING
2008
104views Hardware» more  CIMAGING 2008»
14 years 11 months ago
MCMC curve sampling and geometric conditional simulation
We present an algorithm to generate samples from probability distributions on the space of curves. Traditional curve evolution methods use gradient descent to find a local minimum...
Ayres C. Fan, John W. Fisher III, Jonathan Kane, A...