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Probabilistic Data Programming with ENFrame

Dan Olteanu, Sebastiaan J. van Schaik
2014 IEEE Data Engineering Bulletin  
For inference, the programs are usually grounded to Bayesian networks and fed to MCMC methods [15] .  ...  This paper overviews ENFrame, a programming framework for probabilistic data.  ...  . • The probabilistic event language allows ENFrame to leverage existing work on incremental maintenance of query answers and extend it to incremental maintenance of the program output in the face of updates  ... 
dblp:journals/debu/OlteanuS14 fatcat:ctwdvnshv5ey7ceiwflmamqkuy

C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching [article]

Daniel Ritchie, Andreas Stuhlmüller, Noah D. Goodman
2015 arXiv   pre-print
Lightweight, source-to-source transformation approaches to implementing MCMC for probabilistic programming languages are popular for their simplicity, support of existing deterministic code, and ability  ...  C3 is based on two core ideas: transforming probabilistic programs into continuation passing style (CPS), and caching the results of function calls.  ...  Introduction Probabilistic programming languages (PPLs) are a powerful, general-purpose tool for developing probabilistic models.  ... 
arXiv:1509.02151v2 fatcat:nehtrxyd7jg7bop4t4vwkrxgy4

Querying probabilistic information extraction

Daisy Zhe Wang, Michael J. Franklin, Minos Garofalakis, Joseph M. Hellerstein
2010 Proceedings of the VLDB Endowment  
First, IE is inherently probabilistic, but traditional query processing does not properly handle probabilistic data, resulting in reduced answer quality.  ...  In this paper, we address these two problems by building on an in-database implementation of a leading IE model-Conditional Random Fields using the Viterbi inference algorithm.  ...  Incremental Viterbi Inference The conventional Viterbi top-k inference algorithm is a straightforward adaptation of the dynamic programming recurrence in Equation (2) .  ... 
doi:10.14778/1920841.1920974 fatcat:6roejr6serchniik5zrg76ycse

TP-Compilation for inference in probabilistic logic programs

Jonas Vlasselaer, Guy Van den Broeck, Angelika Kimmig, Wannes Meert, Luc De Raedt
2016 International Journal of Approximate Reasoning  
We propose T P -compilation, a new inference technique for probabilistic logic programs that is based on forward reasoning.  ...  The main difference with existing inference techniques for probabilistic logic programs is that these are a sequence of isolated transformations.  ...  Acknowledgments We wish to thank Bart Bogaerts for useful discussions and Adnan Darwiche and Arthur Choi for support with the SDD package.  ... 
doi:10.1016/j.ijar.2016.06.009 fatcat:fdee5h3lmrghfiixnidfgxq2am

Generating Efficient MCMC Kernels from Probabilistic Programs

Lingfeng Yang, Pat Hanrahan, Noah D. Goodman
2014 International Conference on Artificial Intelligence and Statistics  
are required for inference.  ...  We present a technique that recovers hand-coded levels of performance from a universal probabilistic language, for the Metropolis-Hastings (MH) MCMC inference algorithm.  ...  Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon.  ... 
dblp:conf/aistats/YangHG14 fatcat:iiwegnfct5gslahdc7mqh53nxi

Sound Abstraction and Decomposition of Probabilistic Programs

Steven Holtzen, Guy Van den Broeck, Todd D. Millstein
2018 International Conference on Machine Learning  
We propose a form of sound abstraction for probabilistic programs wherein the abstractions are themselves simplified programs.  ...  Experimentally, we also illustrate the practical benefits of our framework as a tool to decompose probabilistic program inference.  ...  The authors would like to thank Tal Friedman and Jon Aytac for helpful discussions.  ... 
dblp:conf/icml/HoltzenBM18 fatcat:7wa5gnjwjzcmjgn5hgxrq6dr5a

Mixed Logical and Probabilistic Reasoning for Planning and Explanation Generation in Robotics [article]

Zenon Colaco, Mohan Sridharan
2015 arXiv   pre-print
The answer set obtained by solving this program is used for inference, planning, and for jointly explaining (a) unexpected action outcomes due to exogenous actions and (b) partial scene descriptions extracted  ...  For any given task, each action in the plan contained in the answer set is executed probabilistically.  ...  Acknowledgments The authors thank Michael Gelfond and Rashmica Gupta for discussions that contributed to the development of the architecture described in this paper.  ... 
arXiv:1508.00059v1 fatcat:3de7osfs4jeitgl77vwjlzodja

Stochastic Probabilistic Programs [article]

David Tolpin, Tomer Dobkin
2020 arXiv   pre-print
We give several examples of stochastic probabilistic programs, and compare the programs with corresponding deterministic probabilistic programs in terms of model specification and inference.  ...  Stochastic probabilistic programs allow straightforward specification and efficient inference in models with nuisance parameters, noise, and nondeterminism.  ...  Stochastic probabilistic programs facilitate natural specification of probabilistic models, and support inference in the models, for which deterministic probabilistic programs are impossible or difficult  ... 
arXiv:2001.02656v3 fatcat:nxctipjtsnbbhox2trfhpuw7zi

Bayesian Inference of Regular Expressions from Human-Generated Example Strings [article]

Long Ouyang
2018 arXiv   pre-print
We frame regex induction as the problem of inferring a probabilistic regular grammar and propose an efficient inference approach that uses a novel stochastic process recognition model.  ...  In programming by example, users "write" programs by generating a small number of input-output examples and asking the computer to synthesize consistent programs.  ...  By contrast, our method incrementally builds a grammar using probabilistic programming techniques.  ... 
arXiv:1805.08427v2 fatcat:bcfvd36ljjcqhgjr4a54zdae3q

Using Iterative Deepening for Probabilistic Logic Inference [chapter]

Theofrastos Mantadelis, Ricardo Rocha
2016 Lecture Notes in Computer Science  
Tabled exact inference first collects a set of SLG derivations which contain the probabilistic structure of the ProbLog program including the cycles.  ...  We present a novel approach that uses an iterative deepening algorithm in order to perform probabilistic logic inference for ProbLog, a probabilistic extension of Prolog.  ...  Acknowledgments We want to thank the anonymous reviewers for their valuable comments.  ... 
doi:10.1007/978-3-319-51676-9_14 fatcat:4zjk4pxzj5ctnduod7d4nl2hcq

Coarse-to-Fine Sequential Monte Carlo for Probabilistic Programs [article]

Andreas Stuhlmüller, Robert X.D. Hawkins, N. Siddharth, Noah D. Goodman
2015 arXiv   pre-print
We propose an algorithm for transforming probabilistic programs to coarse-to-fine programs which have the same marginal distribution as the original programs, but generate the data at increasing levels  ...  Many practical techniques for probabilistic inference require a sequence of distributions that interpolate between a tractable distribution and an intractable distribution of interest.  ...  Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon.  ... 
arXiv:1509.02962v1 fatcat:s2i2qsnsobf67oajilst5bmknq

Encapsulating models and approximate inference programs in probabilistic modules [article]

Marco F. Cusumano-Towner, Vikash K. Mansinghka
2017 arXiv   pre-print
networks and MCMC and SMC approximate inference programs.  ...  We show that sound approximate inference algorithms can be constructed for networks of probabilistic modules, and we demonstrate that the interface can be implemented using learned stochastic inference  ...  Together, these two procedures enable inference programs to run valid approximate inference algorithms such as MCMC and SMC over the composite probabilistic model.  ... 
arXiv:1612.04759v2 fatcat:a6q5x5qchjg7lpcr7mtsnccks4

Towards an Explanation Generation System for Robots: Analysis and Recommendations

Ben Meadows, Mohan Sridharan, Zenon Colaco
2016 Robotics  
Acknowledgments: The authors thank Pat Langley and Michael Gelfond for discussions related to the systems discussed in this paper. This work was supported in part by the U.S.  ...  Researchers have thus developed algorithms that combine probabilistic and first-order logic representations for abductive reasoning [12] or combine probabilistic and non-monotonic logical reasoning for  ...  logical reasoning and probabilistic reasoning for planning and diagnostics [32, 33] .  ... 
doi:10.3390/robotics5040021 fatcat:hiatuspuzvc2dlta62zdrztwsu

Bayesian causal inference via probabilistic program synthesis [article]

Sam Witty, Alexander Lew, David Jensen, Vikash Mansinghka
2019 arXiv   pre-print
This approach also enables the use of general-purpose inference machinery for probabilistic programs to infer probable causal structures and parameters from data.  ...  Causal inference can be formalized as Bayesian inference that combines a prior distribution over causal models and likelihoods that account for both observations and interventions.  ...  Acknowledgements We thank Javier Burroni, Dan Garant, Zenna Tavares, and Reilly Grant for thoughtful discussion.  ... 
arXiv:1910.14124v1 fatcat:ywbfuyq4cjfzhjrpvqf4tqjhbi

Path Finding under Uncertainty through Probabilistic Inference [article]

David Tolpin, Brooks Paige, Jan Willem van de Meent, Frank Wood
2015 arXiv   pre-print
for this problem.  ...  This approach separates problem representation from the inference algorithm and provides a framework for efficient learning of path-finding policies.  ...  While some algorithms are better suited for certain inference types, most can be used with any valid probabilistic program.  ... 
arXiv:1502.07314v3 fatcat:llo22zjlnfg6ppbdqi6ab73cou
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