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. In this article, the authors propose a framework for performing model comparison of Bayesian models on behavioral data. To do so, they summarize the Bayesian Decision Theory framework, pinpoint areas of non-identifiability, and outline the types of constraints that can be used to make each term in the Bayesian framework identifiable. They then make assumptions to constrain each term in the Bayesian framework, explore how differentiable parameter values are in their model, and apply the technique to two studies that use Bayesian decision theory to explain behavioral responses: time interval estimation and motion perception. Issues of identifiability of internal representations and processes have been prominent issues within cognitive science and psychology for decades.

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We provide a detailed proof for Theorem 1. We provide an alternative proof for identifiability of Poisson BN. I (G D), where the last equality holds because the integrand is the kernel of a beta distribution. The scRNA-seq experiments were performed on five mice with AhR knockout targeted to intestinal stem cells. On average each mouse contributed 6,000 cells.

Multivariate zero-inflated count data arise in a wide range of areas such as economics, social sciences, and biology.

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

In various experiments, our Bayesian SPNs often improve test likelihoods over greedy SPN learners.

Achieving the full promise of the Thermodynamic V ariational Objective (TVO), a recently proposed variational lower bound on the log evidence involving a one-dimensional Riemann integral approximation, requires choosing a "schedule" of sorted discretization points. This paper introduces a bespoke Gaussian process bandit optimization method for automatically choosing these points. Our approach not only automates their one-time selection, but also dynamically adapts their positions over the course of optimization, leading to improved model learning and inference. We provide theoretical guarantees that our bandit optimization converges to the regret-minimizing choice of integration points. Empirical validation of our algorithm is provided in terms of improved learning and inference in V ariational Autoencoders and Sigmoid Belief Networks.