implications for generic particle-based inference methods in probabilistic program- ming systems. David Knowles and Zoubin Ghahramani. Neil Houlsby and Massimiliano Ciaramita. A minimum relative entropy principle for adaptive control in linear quadratic regulators. This insight is developed into a sparse variant of the EIF, called the sparse extended information filters (seif). This makes it possible to use a Gibbs sampler to approximate the distribution over causal structures.
In 1st International Workshop on Traffic Analysis and Classification (iwcmc '10), Caen, France, July 2010. Abstract: We provide a general framework for learning precise, compact, and fast representations of the Bayesian predictive distribution for a model. First, we derive a continuous variant of the Q-learning algorithm, which we call normalized adantage functions (NAF as an alternative to the more commonly used policy gradient and actor-critic methods.
The experimental results show that explicit modeling of dependencies significantly improves accuracy of predictions. Raval, Zoubin Ghahramani, and David. Abstract: We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. In 29th International Conference on Machine Learning, Edinburgh, Scotland, June 2012. Normalized) and input into classifiers. Conference Publications, hyun,. A kernel method for unsupervised structured network inference.
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