Stochastic Probabilistic Programs

• URL: http://arxiv.org/abs/2001.02656v3
• Date: Wed, 22 Jan 2020 16:02:56 GMT
• Title: Stochastic Probabilistic Programs
• Authors: David Tolpin, Tomer Dobkin
• Abstract summary: We introduce the notion of a probabilistic program and present a reference implementation of a probabilistic programming facility supporting specification of programs and inference in them. We give several examples of probabilistic programs, and compare the programs with corresponding deterministic probabilistic programs in terms of model specification and inference.
• Score: 1.90365714903665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce the notion of a stochastic probabilistic program and present a reference implementation of a probabilistic programming facility supporting specification of stochastic probabilistic programs and inference in them. Stochastic probabilistic programs allow straightforward specification and efficient inference in models with nuisance parameters, noise, and nondeterminism. We give several examples of stochastic probabilistic programs, and compare the programs with corresponding deterministic probabilistic programs in terms of model specification and inference. We conclude with discussion of open research topics and related work.

Related papers

• dPASP: A Comprehensive Differentiable Probabilistic Answer Set Programming Environment For Neurosymbolic Learning and Reasoning [0.0]
  We present dPASP, a novel declarative logic programming framework for differentiable neuro-symbolic reasoning. We discuss the several semantics for probabilistic logic programs that can express nondeterministic, contradictory, incomplete and/or statistical knowledge. We then describe an implemented package that supports inference and learning in the language, along with several example programs.
  arXiv  Detail & Related papers  (2023-08-05T19:36:58Z)
• User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems [49.75149094527068]
  We show that diffusion models can be adapted to make predictions and provide uncertainty quantification for chaotic dynamical systems. We develop a probabilistic approximation scheme for the conditional score function which converges to the true distribution as the noise level decreases. We are able to sample conditionally on nonlinear userdefined events at inference time, and matches data statistics even when sampling from the tails of the distribution.
  arXiv  Detail & Related papers  (2023-06-13T03:42:03Z)
• $\omega$PAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs [64.25762042361839]
  $omega$PAP spaces are spaces for reasoning denotationally about expressive differentiable and probabilistic programming languages. Our semantics is general enough to assign meanings to most practical probabilistic and differentiable programs. We establish the almost-everywhere differentiability of probabilistic programs' trace density functions.
  arXiv  Detail & Related papers  (2023-02-21T12:50:05Z)
• Program Analysis of Probabilistic Programs [3.299672391663527]
  dissertation presents three novel techniques to improve probabilistic programming using program analysis. The techniques analyse a probabilistic program and adapt it to make inference more efficient, sometimes in a way that would have been tedious or impossible to do by hand.
  arXiv  Detail & Related papers  (2022-04-14T10:40:54Z)
• Optimal variance-reduced stochastic approximation in Banach spaces [114.8734960258221]
  We study the problem of estimating the fixed point of a contractive operator defined on a separable Banach space. We establish non-asymptotic bounds for both the operator defect and the estimation error.
  arXiv  Detail & Related papers  (2022-01-21T02:46:57Z)
• flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs [25.320181572646135]
  We present a program analysis and associated optimization, flip-hoisting, that collapses repetitious parameters in discrete probabilistic programs to improve inference performance. We implement flip-hoisting in an existing probabilistic programming language and show empirically that it significantly improves inference performance.
  arXiv  Detail & Related papers  (2021-10-19T22:04:26Z)
• Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression [51.770998056563094]
  Probabilistic Gradient Boosting Machines (PGBM) is a method to create probabilistic predictions with a single ensemble of decision trees. We empirically demonstrate the advantages of PGBM compared to existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-03T08:32:13Z)
• Probabilistic Generating Circuits [50.98473654244851]
  We propose probabilistic generating circuits (PGCs) for their efficient representation. PGCs are not just a theoretical framework that unifies vastly different existing models, but also show huge potential in modeling realistic data. We exhibit a simple class of PGCs that are not trivially subsumed by simple combinations of PCs and DPPs, and obtain competitive performance on a suite of density estimation benchmarks.
  arXiv  Detail & Related papers  (2021-02-19T07:06:53Z)
• Tractable Inference in Credal Sentential Decision Diagrams [116.6516175350871]
  Probabilistic sentential decision diagrams are logic circuits where the inputs of disjunctive gates are annotated by probability values. We develop the credal sentential decision diagrams, a generalisation of their probabilistic counterpart that allows for replacing the local probabilities with credal sets of mass functions. For a first empirical validation, we consider a simple application based on noisy seven-segment display images.
  arXiv  Detail & Related papers  (2020-08-19T16:04:34Z)
• Generating Random Logic Programs Using Constraint Programming [12.47276164048813]
  We present a novel approach to generating random logic programs using constraint programming. We show how the model scales with parameter values, and use the model to compare probabilistic inference algorithms across a range of synthetic problems.
  arXiv  Detail & Related papers  (2020-06-02T19:12:53Z)
• Stochastically Differentiable Probabilistic Programs [18.971852464650144]
  The existence of discrete random variables prohibits many basic gradient-based inference engines. We present a novel approach to run inference efficiently and robustly in such programs using Markov Chain Monte Carlo family of algorithms.
  arXiv  Detail & Related papers  (2020-03-02T08:04:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.