The algorithm's computational complexity is polynomial in

We will include a more detailed discussion to motivate our methodology.

Bayesian decision theory outlines a rigorous framework for making optimal decisions based on maximizing expected utility over a model posterior. However, practitioners often do not have access to the full posterior and resort to approximate inference strategies.

They are themselves constructed from i.i.d. Gaussian processes, in analogy to the construction of Wishart or inverse Wishart random variables from i.i.d.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The contribution of this paper is probabilistic programming language that supports parallel inference for graphical models (specifically Bayes nets). Probabilistic programming languages are powerful tools because they allow rapid development of new models without having to derive/implement new inference algorithms. Unlike most existing probabilistic programming languages, Augur produces massively parallel code that can run on a GPU (using CUDA). A unique feature of Augur is that it compiles the model (specified in the language Scala) into an intermediate representation before it's ultimately compiled into a CUDA inference algorithm for parallelization.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper presents a new Gibbs sampler algorithm for FHMMs. The idea is to add an auxillary variable, U, to the state of the Gibbs sampler. The value of U restricts the set of possible values that the hidden state X can take at the next step of the Gibbs sampler. As the number of possible values for X_i is small for each time point i, we can update X given U (and the data) using FFBS. I think this is an original and clever approach to an important class of problems.

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