probability that two proteins belong to the same cluster. The probabilistic model under study is the infinite HMM Beal, Ghahramani and Rasmussen, 2002, in which parameters are learnt using an instance blocked Gibbs sampling, with a step consisting of a dynamic program. We propose a new training strategy and obtain improved generalisation performance and better density estimates in comparative evaluations on several benchmark data sets. Gefcom2012 hierarchical load forecasting: Gradient boosting machines and gaussian processes. In 29th Conference on Uncertainty in Artificial Intelligence, Bellevue, USA, July 2013. In 15th International Conference on Artificial Intelligence and Statistics, pages 255-263, 2012. We present applications of iHMM inference using the beam sampler on changepoint detection and text prediction problems. In ifac Internaltional Conference on Intelligent Control Systems and Signal Processing, volume 1, pages 137-142, 2003.
Department of Pathology, The University of Hong Kong - Events
Experiments show that sparsity comes at the expense of worse predictive distributions. While these methods are guaranteed to be accurate in the limit, samplers for the IBP tend to mix slowly in practice. Abstract: Inspired by "Google Sets we consider the problem of retrieving items from a concept or cluster, given a query consisting of a few items from that cluster. This leads to a variational free utility principle akin to thermodynamical free energy that trades off utility and information costs. Abstract: We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by nonparametric Gaussian process (GP) models. In nips Workshop: Predictive Models in Personalized Medicine Workshop, 2010. Abstract: Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers. Some of these deficiencies can be addressed with the multicanonical ensemble. For simple regression an optimal myopic policy is easily tractable.
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