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NIPS
2003
13 years 10 months ago
Laplace Propagation
We present a novel method for approximate inference in Bayesian models and regularized risk functionals. It is based on the propagation of mean and variance derived from the Lapla...
Alexander J. Smola, Vishy Vishwanathan, Eleazar Es...
NIPS
2000
13 years 10 months ago
Structure Learning in Human Causal Induction
We use graphical models to explore the question of how people learn simple causal relationships from data. The two leading psychological theories can both be seen as estimating th...
Joshua B. Tenenbaum, Thomas L. Griffiths
COGSCI
2008
129views more  COGSCI 2008»
13 years 9 months ago
A Rational Analysis of Rule-Based Concept Learning
We propose a new model of human concept learning that provides a rational analysis for learning of feature-based concepts. This model is built upon Bayesian inference for a gramma...
Noah D. Goodman, Joshua B. Tenenbaum, Jacob Feldma...
AMI
2010
Springer
13 years 6 months ago
Taxi-Aware Map: Identifying and Predicting Vacant Taxis in the City
Knowing where vacant taxis are and will be at a given time and location helps the users in daily planning and scheduling, as well as the taxi service providers in dispatching. In t...
Santi Phithakkitnukoon, Marco Veloso, Carlos Bento...
TCBB
2010
137views more  TCBB 2010»
13 years 3 months ago
The Metropolized Partial Importance Sampling MCMC Mixes Slowly on Minimum Reversal Rearrangement Paths
Markov chain Monte Carlo has been the standard technique for inferring the posterior distribution of genome rearrangement scenarios under a Bayesian approach. We present here a neg...
István Miklós, Bence Melykuti, Krist...