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Tempering for Bayesian C&RT

Nicos Angelopoulos, James Cussens
2005 Proceedings of the 22nd international conference on Machine learning - ICML '05  
Since exact computation is not possible Markov chain Monte Carlo (MCMC) methods are used to produce an approximation.  ...  Full Bayesian inference requires the computation of a posterior over all possible trees.  ...  This work was supported by the UK EPSRC MathFIT project Stochastic Logic Programs for MCMC and also by Applications of Probabilistic Inductive Logic Programming II funded by the European Commission.  ... 
doi:10.1145/1102351.1102354 dblp:conf/icml/AngelopoulosC05 fatcat:xsdpidvb4becvhqeoimo3qkje4

Introduction to the 30th International Conference on Logic Programming Special Issue

MICHAEL LEUSCHEL, TOM SCHRIJVERS
2014 Theory and Practice of Logic Programming  
of Theory and Practice of Logic Programming (TPLP) - the30th International Conference on Logic Programming Special Issue.  ...  The 30th edition of the International Conference of Logic Programming took place in Vienna in July 2014 at the Vienna Summer of Logic - the largest scientific conference in the history of logic.  ...  Several Prolog interpreters are based on the Warren Abstract Machine (WAM), an elegant model to compile Prolog programs.  ... 
doi:10.1017/s1471068414000581 fatcat:6fczd6mhxjcutozkk6t23lvn5e

Integrative Functional Statistics in Logic Programming [chapter]

Nicos Angelopoulos, Vítor Santos Costa, João Azevedo, Jan Wielemaker, Rui Camacho, Lodewyk Wessels
2013 Lecture Notes in Computer Science  
The software is a useful addition to the efforts towards the integration of statistical reasoning and knowledge representation within an AI context.  ...  We present r..eal, a library that integrates the R statistical environment with Prolog. Due to R's functional programming affinity the interface introduced has a minimalistic feel.  ...  Work in this area includes the PRISM system and its EM-based parameter algorithm [16] , Stochastic logic programs with an MCMC structure learning system [2] and the FAM algorithm [9] , the ProbLog  ... 
doi:10.1007/978-3-642-45284-0_13 fatcat:apjn37e5azd4rh67hnjzpajwzq

Bayesian learning of Bayesian networks with informative priors

Nicos Angelopoulos, James Cussens
2008 Annals of Mathematics and Artificial Intelligence  
Each experiment was repeated three times with different random seeds to test the robustness of the MCMC-produced results.  ...  Prior distributions are defined using stochastic logic programs and the MCMC Metropolis-Hastings algorithm is used to (approximately) sample from the posterior.  ...  Acknowledgements This work was partially supported by UK EPSRC MathFIT project Stochastic Logic Programs for MCMC (GR/S30993/01).  ... 
doi:10.1007/s10472-009-9133-x fatcat:yahm6moxr5d2fnzfdh4muusohq

Markov Chain Monte Carlo using Tree-Based Priors on Model Structure [article]

Nicos Angelopoulos, James Cussens
2013 arXiv   pre-print
We have applied this approach to Bayesian net structure learning using a number of priors and tree traversal strategies.  ...  We present a general framework for defining priors on model structure and sampling from the posterior using the Metropolis-Hastings algorithm.  ...  Many thanks to our four anonymous reviewers for their in sightful criticisms of a previous version of this paper.  ... 
arXiv:1301.2254v1 fatcat:jxtsr4xqwfcrblhr5lzyl6l7ye

General-Purpose MCMC Inference over Relational Structures [article]

Brian Milch, Stuart Russell
2012 arXiv   pre-print
Our algorithm gains flexibility by using MCMC states that are only partial descriptions of possible worlds; we provide conditions under which MCMC over partial worlds yields correct answers to queries.  ...  An alternative approach, which we pursue in this paper, is to use a general-purpose probabilistic modeling language (such as BLOG) and a generic Metropolis-Hastings MCMC algorithm that supports user-supplied  ...  Given a probability distribution p on an outcome space Ω, an MCMC algorithm approximates the probability of a query event Q given an evidence event E by generating a sequence of samples s 1 , s 2 , . .  ... 
arXiv:1206.6849v1 fatcat:yzhfsscb6zgrrfysgafq2vidhe

Human-Machine Cooperation: Supporting User Corrections to Automatically Constructed KBs

Michael L. Wick, Karl Schultz, Andrew McCallum
2012 North American Chapter of the Association for Computational Linguistics  
Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsor.  ...  For these experiments, we first create an initial database using the mentions in the "F. Pereira" dataset, and run MCMC until convergence reaching a precision of 80, and F1 of 54.  ...  For inference, we use a modified version of the Metropolis-Hastings algorithm that proposes multiple worlds for each sample (Liu et al., 2000) .  ... 
dblp:conf/naacl/WickSM12 fatcat:3yacqz7tfffzdciiii42ag2rve

On the implementation of the probabilistic logic programming language ProbLog

ANGELIKA KIMMIG, BART DEMOEN, LUC DE RAEDT, VÍTOR SANTOS COSTA, RICARDO ROCHA
2011 Theory and Practice of Logic Programming  
We introduce algorithms that allow the efficient execution of these queries, discuss their implementation on top of the YAP-Prolog system, and evaluate their performance in the context of large networks  ...  ProbLog is a recent probabilistic extension of Prolog motivated by the mining of large biological networks. In ProbLog, facts can be labeled with probabilities.  ...  We would like to thank Hannu Toivonen, Bernd Gutmann and Kristian Kersting for their many contributions to ProbLog, the Biomine team for the application, and Theofrastos Mantadelis for the development of  ... 
doi:10.1017/s1471068410000566 fatcat:q3nliq4bpnca3ohmwsz2ywjaty

Programming Languages and Artificial General Intelligence [chapter]

Vitaly Khudobakhshov, Andrey Pitko, Denis Zotov
2015 Lecture Notes in Computer Science  
Despite the fact that there are thousands of programming languages existing there is a huge controversy about what language is better to solve a particular problem.  ...  Unconventional features (e.g. probabilistic programming and partial evaluation) are discussed as important parts of language design and implementation.  ...  This work was supported by Ministry of Education and Science of the Russian Federation.  ... 
doi:10.1007/978-3-319-21365-1_30 fatcat:3idap27phvfcxo654esn2nls4a

Logic-based representation, reasoning and machine learning for event recognition

Alexander Artikis, Georgios Paliouras, François Portet, Anastasios Skarlatidis
2010 Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems - DEBS '10  
In this paper we review representative approaches of logic-based event recognition, and discuss open research issues of this field.  ...  This requirement may be addressed by employing event recognition systems that detect activities/events of special significance within an organisation, given streams of 'low-level' information that is very  ...  Acknowledgments This work has been partially funded by EU, in the context of the PRONTO project (FP7-ICT 231738).  ... 
doi:10.1145/1827418.1827471 dblp:conf/debs/ArtikisPPS10 fatcat:fwelgrg24bgjznroncdibrhh44

Recent developments in unifying logic and probability

Stuart Russell
2015 Communications of the ACM  
The rules of chess and of many other domains are beyond them.  ...  Even the meaning of such a goal is unclear.  ...  Moreover, the algorithm eliminates isomorphisms under object renumberings by computing the required combinatorial ratios for MCMC transitions between partial worlds of different sizes. 22 MCMC algorithms  ... 
doi:10.1145/2699411 fatcat:envlmg4jdrcbdiciamsctx3r4e

Ultrasound Nerve Segmentation Using Deep Probabilistic Programming

Iresha Rubasinghe, Dulani Meedeniya
2019 Journal of ICT Research and Applications  
This paper discusses an application for analysis of ultrasound nerve segmentation-based biomedical images.  ...  Being an evolving field, there exist only a few expressive programming languages for uncertainty management.  ...  Acknowledgement The authors acknowledge the support received from the Conference & Publishing grant, University of Moratuwa, Sri Lanka for publishing this paper.  ... 
doi:10.5614/itbj.ict.res.appl.2019.13.3.5 fatcat:l7iq4pc6j5cczherrhjzfsskmi

Probabilistic (logic) programming concepts

Luc De Raedt, Angelika Kimmig
2015 Machine Learning  
While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been considered for over 20 years.  ...  A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions  ...  Acknowledgements The authors are indebted to Bernd Gutmann and Ingo Thon for participating in many discussions, and contributing several ideas during the early stages of the research that finally led to  ... 
doi:10.1007/s10994-015-5494-z fatcat:6bgcvas4lfed5invj2jjb7wfmy

Probabilistic Programming Concepts [article]

Luc De Raedt, Angelika Kimmig
2013 arXiv   pre-print
While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been developed since more than 20 years.  ...  A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions  ...  Acknowledgements The authors are indebted to Bernd Gutmann and Ingo Thon for participating in many discussions, and contributing several ideas during the early stages of the research that finally led to  ... 
arXiv:1312.4328v1 fatcat:2zmu2qve2rbapnvsm7zesbbzwy

A comparison of platforms for implementing and running very large scale machine learning algorithms

Zhuhua Cai, Zekai J. Gao, Shangyu Luo, Luis L. Perez, Zografoula Vagena, Christopher Jermaine
2014 Proceedings of the 2014 ACM SIGMOD international conference on Management of data - SIGMOD '14  
We describe an extensive benchmark of platforms available to a user who wants to run a machine learning (ML) inference algorithm over a very large data set, but cannot find an existing implementation and  ...  Our specific contributions are: (1) We shed some light on the relative merits of some (quite different) platforms for implementing large-scale ML algorithms. Our results will surprise many readers.  ...  Material in this paper was supported by the National Science Foundation under grant number 0915315, and the Department of Energy under grant number DE-SC0001779.  ... 
doi:10.1145/2588555.2593680 dblp:conf/sigmod/CaiGLPVJ14 fatcat:a5plm6lchrdinnaj4v2upqfx7u
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