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ORCIDAntonio Salmerón
Antonio Salmerón Cerdán
Person information
- affiliation: University of Almería, Department of Statistics and Applied Mathematics, Almería, Spain
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2020 – today
- 2024
[i5]Rafael Cabañas, Ana D. Maldonado, María Morales, Pedro Aguilera Aguilera, Antonio Salmerón:
Counterfactual Reasoning with Probabilistic Graphical Models for Analyzing Socioecological Systems. CoRR abs/2401.10101 (2024)- 2023
- [j53]Santiago del Rey
, Silverio Martínez-Fernández, Antonio Salmerón:
Bayesian Network analysis of software logs for data-driven software maintenance. IET Softw. 17(3): 268-286 (2023) - 2022
- [c43]Antonio Salmerón, Helge Langseth, Andrés R. Masegosa, Thomas D. Nielsen:
A Reparameterization of Mixtures of Truncated Basis Functions and its Applications. PGM 2022: 205-216 - [e3]Antonio Salmerón, Rafael Rumí:
International Conference on Probabilistic Graphical Models, PGM 2022, 5-7 October 2022, Almería, Spain. Proceedings of Machine Learning Research 186, PMLR 2022 [contents] - 2021
- [j52]Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón:
Probabilistic Models with Deep Neural Networks. Entropy 23(1): 117 (2021) - 2020
- [j51]Ana D. Maldonado, María Morales, Pedro Aguilera Aguilera, Antonio Salmerón:
Analyzing Uncertainty in Complex Socio-Ecological Networks. Entropy 22(1): 123 (2020) - [j50]Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón:
Analyzing concept drift: A case study in the financial sector. Intell. Data Anal. 24(3): 665-688 (2020) - [j49]Javier Cózar, Rafael Cabañas, Antonio Salmerón, Andrés R. Masegosa:
InferPy: Probabilistic modeling with deep neural networks made easy. Neurocomputing 415: 408-410 (2020) - [j48]Andrés R. Masegosa, Antonio Torres, María Morales, Antonio Salmerón:
Comparing two multinomial samples using hierarchical Bayesian models. Prog. Artif. Intell. 9(2): 145-154 (2020) - [j47]Inmaculada Pérez-Bernabé, Ana D. Maldonado, Antonio Salmerón, Thomas D. Nielsen:
MoTBFs: An R Package for Learning Hybrid Bayesian Networks Using Mixtures of Truncated Basis Functions. R J. 12(2): 321 (2020) - [c42]Rafael Cabañas, Javier Cózar, Antonio Salmerón, Andrés R. Masegosa:
Probabilistic Graphical Models with Neural Networks in InferPy. PGM 2020: 601-604
2010 – 2019
- 2019
- [j46]Ana D. Maldonado, Laura Uusitalo, Allan Tucker, Thorsten Blenckner, Pedro Aguilera Aguilera, Antonio Salmerón:
Prediction of a complex system with few data: Evaluation of the effect of model structure and amount of data with dynamic bayesian network models. Environ. Model. Softw. 118: 281-297 (2019) - [j45]Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen:
AMIDST: A Java toolbox for scalable probabilistic machine learning. Knowl. Based Syst. 163: 595-597 (2019) - [j44]Rafael Cabañas, Antonio Salmerón, Andrés R. Masegosa:
InferPy: Probabilistic modeling with Tensorflow made easy. Knowl. Based Syst. 168: 25-27 (2019) - [j43]Mauro Scanagatta, Antonio Salmerón, Fabio Stella:
A survey on Bayesian network structure learning from data. Prog. Artif. Intell. 8(4): 425-439 (2019) - [i4]Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón:
Probabilistic Models with Deep Neural Networks. CoRR abs/1908.03442 (2019) - [i3]Javier Cózar, Rafael Cabañas, Andrés R. Masegosa, Antonio Salmerón:
InferPy: Probabilistic Modeling with Deep Neural Networks Made Easy. CoRR abs/1908.11161 (2019) - 2018
- [j42]Darío Ramos-López, Andrés R. Masegosa, Antonio Salmerón, Rafael Rumí, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen:
Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks. Int. J. Approx. Reason. 100: 115-134 (2018) - [j41]Antonio Salmerón, Rafael Rumí, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen:
A Review of Inference Algorithms for Hybrid Bayesian Networks. J. Artif. Intell. Res. 62: 799-828 (2018) - 2017
- [j40]Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen:
Scaling up Bayesian variational inference using distributed computing clusters. Int. J. Approx. Reason. 88: 435-451 (2017) - [j39]Anders L. Madsen, Frank Jensen, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen:
A parallel algorithm for Bayesian network structure learning from large data sets. Knowl. Based Syst. 117: 46-55 (2017) - [j38]Darío Ramos-López, Andrés R. Masegosa, Ana M. Martínez, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen:
MAP inference in dynamic hybrid Bayesian networks. Prog. Artif. Intell. 6(2): 133-144 (2017) - [c41]Andrés R. Masegosa, Thomas D. Nielsen, Helge Langseth, Darío Ramos-López, Antonio Salmerón, Anders L. Madsen:
Bayesian Models of Data Streams with Hierarchical Power Priors. ICML 2017: 2334-2343 - [i2]Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen:
AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning. CoRR abs/1704.01427 (2017) - [i1]Andrés R. Masegosa, Thomas D. Nielsen, Helge Langseth, Darío Ramos-López, Antonio Salmerón, Anders L. Madsen:
Bayesian Models of Data Streams with Hierarchical Power Priors. CoRR abs/1707.02293 (2017) - 2016
- [j37]Inmaculada Pérez-Bernabé, Antonio Fernández, Rafael Rumí, Antonio Salmerón:
Parameter learning in hybrid Bayesian networks using prior knowledge. Data Min. Knowl. Discov. 30(3): 576-604 (2016) - [j36]Ana D. Maldonado, Pedro Aguilera Aguilera, Antonio Salmerón:
Modeling zero-inflated explanatory variables in hybrid Bayesian network classifiers for species occurrence prediction. Environ. Model. Softw. 82: 31-43 (2016) - [c40]Antonio Salmerón, Anders L. Madsen, Frank Jensen, Helge Langseth, Thomas D. Nielsen, Darío Ramos-López, Ana M. Martínez, Andrés R. Masegosa:
Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs. ECAI 2016: 743-750 - [c39]Rafael Cabañas, Ana M. Martínez, Andrés R. Masegosa, Darío Ramos-López, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen:
Financial Data Analysis with PGMs Using AMIDST. ICDM Workshops 2016: 1284-1287 - [c38]Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen:
d-VMP: Distributed Variational Message Passing. Probabilistic Graphical Models 2016: 321-332 - [c37]Darío Ramos-López, Antonio Salmerón, Rafael Rumí, Ana M. Martínez, Thomas D. Nielsen, Andrés R. Masegosa, Helge Langseth, Anders L. Madsen:
Scalable MAP inference in Bayesian networks based on a Map-Reduce approach. Probabilistic Graphical Models 2016: 415-425 - 2015
- [j35]Concha Bielza, Serafín Moral, Antonio Salmerón:
Recent Advances in Probabilistic Graphical Models. Int. J. Intell. Syst. 30(3): 207-208 (2015) - [j34]Prakash P. Shenoy, Rafael Rumí, Antonio Salmerón:
Practical Aspects of Solving Hybrid Bayesian Networks Containing Deterministic Conditionals. Int. J. Intell. Syst. 30(3): 265-291 (2015) - [j33]Ana D. Maldonado, Rosa F. Ropero, Pedro Aguilera Aguilera, Rafael Rumí, Antonio Salmerón:
Continuous Bayesian networks for the estimation of species richness. Prog. Artif. Intell. 4(3-4): 49-57 (2015) - [c36]Anders L. Madsen, Frank Jensen, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen:
Parallelisation of the PC Algorithm. CAEPIA 2015: 14-24 - [c35]Antonio Salmerón, Darío Ramos-López, Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Antonio Fernández, Helge Langseth, Anders L. Madsen, Thomas D. Nielsen:
Parallel Importance Sampling in Conditional Linear Gaussian Networks. CAEPIA 2015: 36-46 - [c34]Ana D. Maldonado, Rosa F. Ropero, Pedro Aguilera Aguilera, Rafael Rumí, Antonio Salmerón:
Estimation of Species Richness Using Bayesian Networks. CAEPIA 2015: 153-163 - [c33]Inmaculada Pérez-Bernabé, Antonio Salmerón, Helge Langseth:
Learning Conditional Distributions Using Mixtures of Truncated Basis Functions. ECSQARU 2015: 397-406 - [c32]Antonio Salmerón, Rafael Rumí, Helge Langseth, Anders L. Madsen, Thomas D. Nielsen:
MPE Inference in Conditional Linear Gaussian Networks. ECSQARU 2015: 407-416 - [c31]Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez:
Modeling Concept Drift: A Probabilistic Graphical Model Based Approach. IDA 2015: 72-83 - [c30]Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez:
Dynamic Bayesian modeling for risk prediction in credit operations. SCAI 2015: 17-26 - 2014
- [j32]Jens Dalgaard Nielsen, Antonio Salmerón, José A. Gámez:
A tool based on Bayesian networks for supporting geneticists in plant improvement by controlled pollination. Int. J. Approx. Reason. 55(1): 74-83 (2014) - [j31]Helge Langseth, Thomas D. Nielsen, Inmaculada Pérez-Bernabé, Antonio Salmerón:
Learning mixtures of truncated basis functions from data. Int. J. Approx. Reason. 55(4): 940-956 (2014) - [j30]Antonio Fernández, José A. Gámez, Rafael Rumí, Antonio Salmerón:
Data clustering using hidden variables in hybrid Bayesian networks. Prog. Artif. Intell. 2(2-3): 141-152 (2014) - [c29]Thomas D. Nielsen, Sigve Hovda, Antonio Fernández, Helge Langseth, Anders L. Madsen, Andrés R. Masegosa, Antonio Salmerón:
Requirement Engineering for a Small Project with Pre-Specified Scope. NIK 2014 - [c28]Antonio Fernández, Rafael Rumí, José del Sagrado, Antonio Salmerón:
Supervised Classification Using Hybrid Probabilistic Decision Graphs. Probabilistic Graphical Models 2014: 206-221 - [c27]Anders L. Madsen, Frank Jensen, Antonio Salmerón, Martin Karlsen, Helge Langseth, Thomas D. Nielsen:
A New Method for Vertical Parallelisation of TAN Learning Based on Balanced Incomplete Block Designs. Probabilistic Graphical Models 2014: 302-317 - 2013
- [j29]Irene Martínez, Serafín Moral, Carmelo Rodríguez, Antonio Salmerón:
New strategies for finding multiplicative decompositions of probability trees. Appl. Math. Comput. 225: 573-589 (2013) - [j28]Barry R. Cobb, Rafael Rumí, Antonio Salmerón:
Inventory management with log-normal demand per unit time. Comput. Oper. Res. 40(7): 1842-1851 (2013) - [j27]Andrés Cano, Manuel Gómez-Olmedo, Serafín Moral, Cora B. Pérez-Ariza, Antonio Salmerón:
Inference in Bayesian Networks with Recursive Probability Trees: Data Structure Definition and Operations. Int. J. Intell. Syst. 28(7): 623-647 (2013) - [c26]Andrés Cano, Manuel Gómez-Olmedo, Serafín Moral, Cora Beatriz Pérez-Ariza, Antonio Salmerón:
Learning Recursive Probability Trees from Data. CAEPIA 2013: 332-341 - [c25]Antonio Fernández, Inmaculada Pérez-Bernabé, Antonio Salmerón:
On Using the PC Algorithm for Learning Continuous Bayesian Networks: An Experimental Analysis. CAEPIA 2013: 342-351 - [c24]Antonio Fernández, Inmaculada Pérez-Bernabé, Rafael Rumí, Antonio Salmerón:
Incorporating Prior Knowledge when Learning Mixtures of Truncated Basis Functions from Data. SCAI 2013: 95-104 - [e2]Concha Bielza, Antonio Salmerón, Amparo Alonso-Betanzos, José Ignacio Hidalgo, Luis Martínez-López, Alicia Troncoso Lora, Emilio Corchado, Juan M. Corchado:
Advances in Artificial Intelligence - 15th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2013, Madrid, Spain, September 17-20, 2013. Proceedings. Lecture Notes in Computer Science 8109, Springer 2013, ISBN 978-3-642-40642-3 [contents] - 2012
- [j26]Enrique de Amo, Manuel Díaz Carrillo, Juan Fernández-Sánchez, Antonio Salmerón:
Moments and associated measures of copulas with fractal support. Appl. Math. Comput. 218(17): 8634-8644 (2012) - [j25]Antonio Fernández, Rafael Rumí, Antonio Salmerón:
Answering queries in hybrid Bayesian networks using importance sampling. Decis. Support Syst. 53(3): 580-590 (2012) - [j24]Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón:
Mixtures of truncated basis functions. Int. J. Approx. Reason. 53(2): 212-227 (2012) - [j23]Jens Dalgaard Nielsen, José A. Gámez, Antonio Salmerón:
Modelling and inference with Conditional Gaussian Probabilistic Decision Graphs. Int. J. Approx. Reason. 53(7): 929-945 (2012) - [j22]Andrés Cano, Manuel Gómez-Olmedo, Serafín Moral, Cora B. Pérez-Ariza, Antonio Salmerón:
Learning recursive probability trees from probabilistic potentials. Int. J. Approx. Reason. 53(9): 1367-1387 (2012) - [j21]Andrés Cano, Manuel Gómez-Olmedo, Cora B. Pérez-Ariza, Antonio Salmerón:
Fast Factorisation of Probabilistic Potentials and its Application to Approximate Inference in Bayesian Networks. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 20(2): 223-243 (2012) - 2011
- [j20]Pedro Aguilera Aguilera, Antonio Fernández, Rosa Fernández, Rafael Rumí, Antonio Salmerón:
Bayesian networks in environmental modelling. Environ. Model. Softw. 26(12): 1376-1388 (2011) - [j19]Antonio Fernández, María Morales, Carmelo Rodríguez, Antonio Salmerón:
A system for relevance analysis of performance indicators in higher education using Bayesian networks. Knowl. Inf. Syst. 27(3): 327-344 (2011) - [c23]M. Julia Flores, José A. Gámez, Ana M. Martínez, Antonio Salmerón:
Mixture of truncated exponentials in supervised classification: Case study for the naive bayes and averaged one-dependence estimators classifiers. ISDA 2011: 593-598 - [c22]Prakash P. Shenoy, Rafael Rumí, Antonio Salmerón:
Some practical issues in inference in hybrid Bayesian networks with deterministic conditionals. ISDA 2011: 605-610 - 2010
- [j18]Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón:
Parameter estimation and model selection for mixtures of truncated exponentials. Int. J. Approx. Reason. 51(5): 485-498 (2010) - [j17]Jens Dalgaard Nielsen, Rafael Rumí, Antonio Salmerón:
Structural-EM for learning PDG models from incomplete data. Int. J. Approx. Reason. 51(5): 515-530 (2010) - [j16]Antonio Fernández, Jens Dalgaard Nielsen, Antonio Salmerón:
Learning Bayesian Networks for Regression from Incomplete Databases. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 18(1): 69-86 (2010) - [c21]Jens Dalgaard Nielsen, Antonio Salmerón Cerdán:
Conditional Gaussian Probabilistic Decision Graphs. FLAIRS 2010 - [c20]Andrés Cano, Manuel Gómez-Olmedo, Cora B. Pérez-Ariza, Antonio Salmerón:
Fast Factorization of Probability Trees and Its Application to Recursive Trees Learning. SMPS 2010: 65-72 - [c19]Irene Martínez, Carmelo Rodríguez, Antonio Salmerón:
Probability Tree Factorisation with Median Free Term. SMPS 2010: 457-465
2000 – 2009
- 2009
- [j15]Jens Dalgaard Nielsen, Rafael Rumí, Antonio Salmerón:
Supervised classification using probabilistic decision graphs. Comput. Stat. Data Anal. 53(4): 1299-1311 (2009) - [j14]Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón:
Inference in hybrid Bayesian networks. Reliab. Eng. Syst. Saf. 94(10): 1499-1509 (2009) - [c18]Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón:
Maximum Likelihood Learning of Conditional MTE Distributions. ECSQARU 2009: 240-251 - [c17]Barry R. Cobb, Rafael Rumí, Antonio Salmerón:
Predicting Stock and Portfolio Returns Using Mixtures of Truncated Exponentials. ECSQARU 2009: 781-792 - 2008
- [j13]Antonio Fernández, Antonio Salmerón:
BayesChess: A computer chess program based on Bayesian networks. Pattern Recognit. Lett. 29(8): 1154-1159 (2008) - [c16]Antonio Fernández, Antonio Salmerón:
Extension of Bayesian Network Classifiers to Regression Problems. IBERAMIA 2008: 83-92 - 2007
- [j12]Rafael Rumí, Antonio Salmerón:
Approximate probability propagation with mixtures of truncated exponentials. Int. J. Approx. Reason. 45(2): 191-210 (2007) - [j11]María Morales, Carmelo Rodríguez, Antonio Salmerón:
Selective Naive Bayes for Regression Based on Mixtures of Truncated Exponentials. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 15(6): 697-716 (2007) - [c15]Antonio Fernández, María Morales, Antonio Salmerón:
Tree Augmented Naive Bayes for Regression Using Mixtures of Truncated Exponentials: Application to Higher Education Management. IDA 2007: 59-69 - 2006
- [j10]Peter J. F. Lucas, José A. Gámez, Antonio Salmerón:
Special issue on PGM'04: Second European workshop on probabilistic graphical models 2004. Int. J. Approx. Reason. 42(1-2): 1-3 (2006) - [j9]Vanessa Romero, Rafael Rumí, Antonio Salmerón:
Learning hybrid Bayesian networks using mixtures of truncated exponentials. Int. J. Approx. Reason. 42(1-2): 54-68 (2006) - [c14]José A. Gámez, Rafael Rumí, Antonio Salmerón:
Unsupervised naive Bayes for data clustering with mixtures of truncated exponentials. Probabilistic Graphical Models 2006: 123-130 - [c13]Irene Martínez, Carmelo Rodríguez, Antonio Salmerón:
Dynamic importance sampling in Bayesian networks using factorisation of probability trees. Probabilistic Graphical Models 2006: 187-194 - 2005
- [j8]Serafín Moral, Antonio Salmerón:
Dynamic importance sampling in Bayesian networks based on probability trees. Int. J. Approx. Reason. 38(3): 245-261 (2005) - [c12]Rafael Rumí, Antonio Salmerón:
Penniless Propagation with Mixtures of Truncated Exponentials. ECSQARU 2005: 39-50 - [c11]Irene Martínez, Serafín Moral, Carmelo Rodríguez, Antonio Salmerón:
Approximate Factorisation of Probability Trees. ECSQARU 2005: 51-62 - [c10]Barry R. Cobb, Rafael Rumí, Antonio Salmerón:
Modeling Conditional Distributions of Continuous Variables in Bayesian Networks. IDA 2005: 36-45 - 2003
- [j7]José A. Gámez, Antonio Salmerón:
Probabilistic graphical models. Int. J. Intell. Syst. 18(2): 149-151 (2003) - [j6]Andrés Cano, Serafín Moral, Antonio Salmerón:
Novel strategies to approximate probability trees in penniless propagation. Int. J. Intell. Syst. 18(2): 193-203 (2003) - [c9]José del Sagrado, Antonio Salmerón:
Representing Canonical Models as Probability Trees. CAEPIA 2003: 478-487 - [c8]Serafín Moral, Antonio Salmerón:
Dynamic Importance Sampling Computation in Bayesian Networks. ECSQARU 2003: 137-148 - [c7]Serafín Moral, Rafael Rumí, Antonio Salmerón:
Approximating Conditional MTE Distributions by Means of Mixed Trees. ECSQARU 2003: 173-183 - 2002
- [j5]Andrés Cano, Serafín Moral, Antonio Salmerón:
Different strategies to approximate probability trees in penniless propagation. Inteligencia Artif. 6(15) (2002) - [j4]Andrés Cano, Serafín Moral, Antonio Salmerón:
Lazy evaluation in penniless propagation over join trees. Networks 39(4): 175-185 (2002) - [c6]Irene Martínez, Serafín Moral, Carmelo Rodríguez, Antonio Salmerón:
Factorisation of Probability Trees and its Application to Inference in Bayesian Networks. Probabilistic Graphical Models 2002 - [c5]Serafín Moral, Rafael Rumí, Antonio Salmerón:
Estimating Mixtures of Truncated Exponentials from Data. Probabilistic Graphical Models 2002 - [e1]José A. Gámez, Antonio Salmerón:
First European Workshop on Probabilistic Graphical Models, 6-8 November - 2002 - Cuenca (Spain), Electronic Proceedings. 2002 [contents] - 2001
- [c4]Serafín Moral, Rafael Rumí, Antonio Salmerón:
Mixtures of Truncated Exponentials in Hybrid Bayesian Networks. ECSQARU 2001: 156-167 - [c3]Antonio Salmerón, Serafín Moral:
Importance Sampling in Bayesian Networks Using Antithetic Variables. ECSQARU 2001: 168-179 - 2000
- [j3]Andrés Cano, Serafín Moral, Antonio Salmerón:
Penniless propagation in join trees. Int. J. Intell. Syst. 15(11): 1027-1059 (2000) - [j2]Fernando Reche, Antonio Salmerón:
Operational Approach to General Fuzzy Measures. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 8(3): 369-382 (2000)
1990 – 1999
- 1999
- [c2]Serafín Moral, Antonio Salmerón:
A Monte Carlo Algorithm for Combining Dempster-Shafer Belief Based on Approximate Pre-computation. ESCQARU 1999: 305-315 - [c1]Fernando Reche, Antonio Salmerón:
Towards an Operational Interpretation of Fuzzy Measures. ISIPTA 1999: 312-318 - 1998
- [j1]Luis D. Hernández, Serafín Moral, Antonio Salmerón:
A Monte Carlo algorithm for probabilistic propagation in belief networks based on importance sampling and stratified simulation techniques. Int. J. Approx. Reason. 18(1-2): 53-91 (1998)
Coauthor Index
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