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Approximate lifted inference with probabilistic databases

2015
*
Proceedings of the VLDB Endowment
*

This paper proposes a new approach for

doi:10.14778/2735479.2735494
fatcat:safh6aiiwfdlxpitp3bipdxg7y
*approximate*evaluation of #P-hard queries*with**probabilistic**databases*. ... We give a detailed experimental evaluation of our approach and, in the process, provide a new way of thinking about the value of*probabilistic*methods over non-*probabilistic*methods for ranking query answers ...*Lifted*and*approximate**inference*. ...##
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Approximate Lifted Inference with Probabilistic Databases
[article]

2014
*
arXiv
*
pre-print

This paper proposes a new approach for

arXiv:1412.1069v1
fatcat:pqzm45dp5rh6njv5wrybpdj3xq
*approximate*evaluation of #P-hard queries*with**probabilistic**databases*. ... We give a detailed experimental evaluation of our approach and, in the process, provide a new way of thinking about the value of*probabilistic*methods over non-*probabilistic*methods for ranking query answers ...*Lifted*and*approximate**inference*. ...##
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10 Years of Probabilistic Querying – What Next?
[chapter]

2013
*
Lecture Notes in Computer Science
*

techniques based on knowledge compilation and

doi:10.1007/978-3-642-40683-6_1
fatcat:lofuquzqgbb4hcjtjeqydyakbe
*lifted*(first-order)*inference*. ... Over the past decade, the two research areas of*probabilistic**databases*and*probabilistic*programming have intensively studied the problem of making structured*probabilistic**inference*scalable, but-so ...*Probabilistic*programming provides sophisticated knowledge compilation techniques and initial approaches for*lifted*(first-order)*inference*,*with*judiciously tuned*approximation*algorithms for the cases ...##
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Query Processing over Uncertain Data
[chapter]

2017
*
Encyclopedia of Database Systems
*

In the

doi:10.1007/978-1-4899-7993-3_80689-1
fatcat:fesows5udzcgpcihitkwzlpeze
*probabilistic*setting, this problem becomes the computation of all pairs (t, p), where the tuple t is in the query answer in some random world of the input*probabilistic**database**with*probability ... SYNONYMS Query Processing over*Probabilistic*Data DEFINITION An uncertain or*probabilistic**database*is defined as a probability distribution over a set of deterministic*database*instances called possible ... SPROUT pioneered in-and out-*database*techniques based on lin-eage compilation [11] . Reminiscent of*lifted**inference*in AI, follow-up work*lifts*compilation to first-order lineage [9] . ...##
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Scaling Lifted Probabilistic Inference and Learning Via Graph Databases

2016
*
Proceedings of the 2016 SIAM International Conference on Data Mining
*

This paper investigates whether 'Compilation to Graph

doi:10.1137/1.9781611974348.83
dblp:conf/sdm/DasWKKN16
fatcat:6ng7pbllvva2vh3b2fcuqfeaaa
*Databases*' could be a practical technique for scaling*lifted**probabilistic**inference*and learning methods. ... One of the key operations inside these*lifted*approaches is counting -be it for parameter/structure learning or for efficient*inference*. ...*Lifted**Inference*As a final task, given the recent surge in interest in the so-called*lifted**probabilistic**inference*methods, we employed our*approximation*strategy for counting in one such method. ...##
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State-Space Abstractions for Probabilistic Inference: A Systematic Review
[article]

2018
*
arXiv
*
pre-print

Algorithms that exploit those symmetries have been devised in different research fields, for example by the

arXiv:1804.06748v2
fatcat:yj2eafizonaqvcg7k4e24tacya
*lifted**inference*-, multiple object tracking-, and modeling and simulation-communities. ... Researchers from different fields that are confronted*with*a state space explosion problem in a*probabilistic*system can use this classification to identify possible solutions. ... Several other search-*Probabilistic**Databases*Ideas related to*lifted**inference*arose independently in the*probabilistic**database*community. ...##
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Query Processing on Probabilistic Data: A Survey

2017
*
Foundations and Trends in Databases
*

SlimShot SlimShot was introduced by Gribkoff and Suciu [2016] as a

doi:10.1561/1900000052
fatcat:jzifdhyvsnh7thqrnuptxbpejy
*probabilistic**database*system that combines*lifted**inference**with**approximate*weighted model counting based on Monte Carlo simulations ... On the theoretical side, one is to study the complexity of*approximate**lifted**inference*. ... Recall that the*database*schema is R = (R 1 , R 2 , . . . , R ). Consider the set S = {R 1 , . . . , R } ∪ [n]*with*the total order R 1 < R 2 < · · · < R < 1 < 2 < · · · < n. ...##
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Bisimulation-based Approximate Lifted Inference
[article]

2012
*
arXiv
*
pre-print

Here, we describe

arXiv:1205.2616v1
fatcat:xxzrbs4idvhkpe5p523gbfvdx4
*lifted**inference*algorithms that determine symmetries and automatically*lift*the*probabilistic*model to speedup*inference*. ... We report experiments on both synthetic and real data to show that in the presence of symmetries, run-times for*inference*can be improved significantly,*with**approximate**lifted**inference*providing orders ... We would also like to thank the anonymous reviewers for their comments and suggestions and Parag Singla for sharing*with*us the Cora-ER MLN. ...##
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SlimShot

2016
*
Proceedings of the VLDB Endowment
*

SlimShot converts the MLN to a tuple-independent

doi:10.14778/2904483.2904487
fatcat:6d3qzzzeczcgnnip2uhormrcka
*probabilistic**database*, then uses a simple Monte Carlo-based*inference*,*with*three key enhancements: (1) it combines sampling*with*safe query evaluation ... In this paper we describe SlimShot (Scalable*Lifted**Inference*and Monte Carlo Sampling Hybrid Optimization Technique), a*probabilistic**inference*engine for knowledge bases. ... (8) , which consists of combining sampling*with**lifted**probabilistic**inference*. ...##
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Evaluating Inference Algorithms for the Prolog Factor Language
[chapter]

2013
*
Lecture Notes in Computer Science
*

PFL is also capable of solving

doi:10.1007/978-3-642-38812-5_6
fatcat:zmarg5erd5de7owelpw3pkyv54
*probabilistic*queries on these models through the implementation of four*inference*algorithms: variable elimination, belief propagation,*lifted*variable elimination and*lifted*... Over the last years there has been some interest in models that combine first-order logic and*probabilistic*graphical models to describe large scale domains, and in efficient ways to perform*inference*... First, we would like to use the PFL to understand the general usefulness of*lifted**inference*and how it plays*with*logical and*probabilistic**inference*. ...##
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Lifted generative learning of Markov logic networks

2015
*
Machine Learning
*

This in turn requires performing

doi:10.1007/s10994-015-5532-x
fatcat:ifzxvwghxfbyndujkqmarwmpw4
*probabilistic**inference*, which, in general, is intractable in MLNs.*Lifted**inference*speeds up*probabilistic**inference*by exploiting symmetries in a model. ... First, we provide a generic algorithm for learning maximum likelihood weights that works*with*any exact*lifted**inference*approach. ... speed up*probabilistic**inference*. ...##
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Learning, Logic, and Probability: A Unified View
[chapter]

2004
*
Lecture Notes in Computer Science
*

Overview Motivation Background Markov logic

doi:10.1007/978-3-540-30109-7_26
fatcat:bcnvefkpizhx5o2ojubolrz6ca
*Inference* Learning Software Applications Discussion Logical and Statistical AI Field Logical approach Statistical approach Knowledge representation ... graphical models and first-order logic are special cases Unified*inference*algorithms Unified learning algorithms Easy-to-use software Broad applicability A new kind of programming language ... Can KBMC, lazy and*lifted**inference*be combined? Can we have*lifted**inference*over both*probabilistic*and deterministic dependencies? (*Lifted*MC-SAT?) ...##
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Learning, Logic, and Probability: A Unified View
[chapter]

2006
*
Lecture Notes in Computer Science
*

Overview Motivation Background Markov logic

doi:10.1007/11874850_2
fatcat:pwtbgmnberfv5apufkkvygpbu4
*Inference* Learning Software Applications Discussion Logical and Statistical AI Field Logical approach Statistical approach Knowledge representation ... graphical models and first-order logic are special cases Unified*inference*algorithms Unified learning algorithms Easy-to-use software Broad applicability A new kind of programming language ... Can KBMC, lazy and*lifted**inference*be combined? Can we have*lifted**inference*over both*probabilistic*and deterministic dependencies? (*Lifted*MC-SAT?) ...##
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Learning, Logic, and Probability: A Unified View
[chapter]

2006
*
Lecture Notes in Computer Science
*

Overview Motivation Background Markov logic

doi:10.1007/978-3-540-36668-3_1
fatcat:7xyuyldeyrfjdhsriy5rosc3re
*Inference* Learning Software Applications Discussion Logical and Statistical AI Field Logical approach Statistical approach Knowledge representation ... graphical models and first-order logic are special cases Unified*inference*algorithms Unified learning algorithms Easy-to-use software Broad applicability A new kind of programming language ... Can KBMC, lazy and*lifted**inference*be combined? Can we have*lifted**inference*over both*probabilistic*and deterministic dependencies? (*Lifted*MC-SAT?) ...##
###
Learning, Logic, and Probability: A Unified View
[chapter]

2006
*
Lecture Notes in Computer Science
*

*Inference* Learning Software Applications Discussion Logical and Statistical AI Field Logical approach Statistical approach Knowledge representation ... graphical models and first-order logic are special cases Unified

*inference*algorithms Unified learning algorithms Easy-to-use software Broad applicability A new kind of programming language ... Can KBMC, lazy and

*lifted*

*inference*be combined? Can we have

*lifted*

*inference*over both

*probabilistic*and deterministic dependencies? (

*Lifted*MC-SAT?) ...

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