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Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach
We present an exact Bayesian inference method for discrete statistical models, which can find exact solutions to a large class of discrete inference problems, even with infinite support and continuous priors.
Visualizing Quantum Circuit Probability -- estimating computational action for quantum program synthesis
This research applies concepts from algorithmic probability to Boolean and quantum combinatorial logic circuits.
Quantum Physics Computational Complexity Information Theory Information Theory
diff-SAT -- A Software for Sampling and Probabilistic Reasoning for SAT and Answer Set Programming
This paper describes diff-SAT, an Answer Set and SAT solver which combines regular solving with the capability to use probabilistic clauses, facts and rules, and to sample an optimal world-view (multiset of satisfying Boolean variable assignments or answer sets) subject to user-provided probabilistic constraints.
Fast calculation of p-values for one-sided Kolmogorov-Smirnov type statistics
The method is based on a reduction to the boundary-crossing probability of a pure jump stochastic process.
Computation 62E15, 62-04, 62F03, 60G51, 62G30 G.3
Automatic structured variational inference
However, the performance of the variational approach depends on the choice of an appropriate variational family.
Inference with selection, varying population size and evolving population structure: Application of ABC to a forward-backward coalescent process with interactions
Second, we develop an Approximate Bayesian Computation framework to use our model for analyzing genetic data.
Populations and Evolution Probability Applications 92D25, 60J80, 92D15, 60J75
Pyro: Deep Universal Probabilistic Programming
Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research.
TensorFlow Distributions
The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation.
Mesh: Compacting Memory Management for C/C++ Applications
Programs written in C/C++ can suffer from serious memory fragmentation, leading to low utilization of memory, degraded performance, and application failure due to memory exhaustion.
Programming Languages Data Structures and Algorithms Performance
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes
Datasets with hundreds of variables and many missing values are commonplace.
