Probabilistic Programming
87 papers with code • 0 benchmarks • 0 datasets
Probabilistic programming languages are designed to describe probabilistic models and then perform inference in those models. PPLs are closely related to graphical models and Bayesian networks, but are more expressive and flexible.
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Use these libraries to find Probabilistic Programming models and implementationsLatest papers with no code
Automated Efficient Estimation using Monte Carlo Efficient Influence Functions
We prove that MC-EIF is consistent, and that estimators using MC-EIF achieve optimal $\sqrt{N}$ convergence rates.
Efficient Incremental Belief Updates Using Weighted Virtual Observations
We present an algorithmic solution to the problem of incremental belief updating in the context of Monte Carlo inference in Bayesian statistical models represented by probabilistic programs.
Statistical Learning of Conjunction Data Messages Through a Bayesian Non-Homogeneous Poisson Process
In fact, the rate at which the CDMs are issued depends on the behaviour of the objects as well as on the screening process performed by third parties.
Worst-Case Analysis is Maximum-A-Posteriori Estimation
The worst-case resource usage of a program can provide useful information for many software-engineering tasks, such as performance optimization and algorithmic-complexity-vulnerability discovery.
Inferring Capabilities from Task Performance with Bayesian Triangulation
As machine learning models become more general, we need to characterise them in richer, more meaningful ways.
Pearl's and Jeffrey's Update as Modes of Learning in Probabilistic Programming
In terms of categorical probability theory, this amounts to an analysis of the situation in terms of the behaviour of the multiset functor, extended to the Kleisli category of the distribution monad.
From Probabilistic Programming to Complexity-based Programming
The paper presents the main characteristics and a preliminary implementation of a novel computational framework named CompLog.
Scaling Integer Arithmetic in Probabilistic Programs
Distributions on integers are ubiquitous in probabilistic modeling but remain challenging for many of today's probabilistic programming languages (PPLs).
Towards an architectural framework for intelligent virtual agents using probabilistic programming
Probabilistic models in KorraAI are used to model its behavior and interactions with the user.
A Heavy-Tailed Algebra for Probabilistic Programming
Despite the successes of probabilistic models based on passing noise through neural networks, recent work has identified that such methods often fail to capture tail behavior accurately, unless the tails of the base distribution are appropriately calibrated.
