By tailoring a multi-dimensional space (or multi-dimensional array) into a number of rectangular regions, the partition model can fit data using these "blocks" such that the data within each block

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Inference amortization methods share information across multiple posterior-inference problems, allowing each to be carried out more efficiently.

What policy should be employed in a Markov decision process with uncertain parameters? Robust optimization's answer to this question is to use rectangular

We present an approach for simultaneously separating and localizing multiple sound sources using recorded microphone data.

Based on non-local prior distributions, we propose a Bayesian model selection (BMS) procedure for boundary detection in a sequence of data with multiple systematic mean changes. The BMS method can effectively suppress the non-boundary spike points with large instantaneous changes.

Neural Information Processing Systems http://nips.cc/

It has been found that DBM captures human behavior better than FBM, even though the latter more veridically captures experimental design in a variety of tasks, e.g.

Posterior distributions depend on the modeling assumptions and can rarely be computed exactly. V ariational Inference (VI) is a technique to approximate posterior distributions through optimization. It involves choosing a set of tractable densities, a.k.a.

In many areas of science, we strive to answer questions that are fundamentally causal in nature.