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BETA
Peña, Jose M.
Alternative names
Publications (10 of 51) Show all publications
Bendtsen, M. & Peña, J. M. (2017). Modelling regimes with Bayesian network mixtures. In: Proceedings of the Thirtieth Annual Workshop of the Swedish Artificial Intelligence Society: . Paper presented at The Thirtieth Annual Workshop of the Swedish Artificial Intelligence Society - SAIS 2017 (pp. 20-29).
Open this publication in new window or tab >>Modelling regimes with Bayesian network mixtures
2017 (English)In: Proceedings of the Thirtieth Annual Workshop of the Swedish Artificial Intelligence Society, 2017, p. 20-29Conference paper, Published paper (Refereed)
Abstract [en]

Bayesian networks (BNs) are advantageous when representing single independence models, however they do not allow us to model changes among the relationships of the random variables over time. Due to such regime changes, it may be necessary to use different BNs at different times in order to have an appropriate model over the random variables. In this paper we propose two extensions to the traditional hidden Markov model, allowing us to represent both the different regimes using different BNs, and potential driving forces behind the regime changes, by modelling potential dependence between state transitions and some observable variables. We show how expectation maximisation can be used to learn the parameters of the proposed model, and run both synthetic and real-world experiments to show the model’s potential.

Keywords
Bayesian networks, hidden Markov models, regimes, algorithmic trading
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-137664 (URN)
Conference
The Thirtieth Annual Workshop of the Swedish Artificial Intelligence Society - SAIS 2017
Available from: 2017-05-24 Created: 2017-05-24 Last updated: 2018-01-13Bibliographically approved
Peña, J. M. (2016). Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphs. In: Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016): . Paper presented at The 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), New York City, NY, USA, June 25-29, 2016.
Open this publication in new window or tab >>Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphs
2016 (English)In: Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), 2016Conference paper, Published paper (Refereed)
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-141894 (URN)978-0-9966431-1-5 (ISBN)
Conference
The 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), New York City, NY, USA, June 25-29, 2016
Available from: 2017-10-12 Created: 2017-10-12 Last updated: 2018-01-13Bibliographically approved
Bendtsen, M. & Peña, J. M. (2016). Gated Bayesian Networks for Algorithmic Trading. International Journal of Approximate Reasoning, 69, 58-80
Open this publication in new window or tab >>Gated Bayesian Networks for Algorithmic Trading
2016 (English)In: International Journal of Approximate Reasoning, ISSN 0888-613X, E-ISSN 1873-4731, Vol. 69, p. 58-80Article in journal (Refereed) Published
Abstract [en]

Gated Bayesian networks (GBNs) are a recently introduced extension of Bayesian networks that aims to model dynamical systems consisting of several distinct phases. In this paper, we present an algorithm for semi-automatic learning of GBNs. We use the algorithm to learn GBNs that output buy and sell decisions for use in algorithmic trading systems. We show how using the learnt GBNs can substantially lower risks towards invested capital, while at the same time generating similar or better rewards, compared to the benchmark investment strategy buy-and-hold.

Place, publisher, year, edition, pages
Elsevier: , 2016
Keywords
Probabilistic graphical models, Bayesian networks, algorithmic
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-124066 (URN)10.1016/j.ijar.2015.11.002 (DOI)000368957000004 ()
Note

Funding agencies: Center for Industrial Information Technology, Linkoping University (CENIIT) [09.01]; Swedish Research Council [2010-4808]

Available from: 2016-01-19 Created: 2016-01-19 Last updated: 2018-01-10
Peña, J. M. (2016). Learning Acyclic Directed Mixed Graphs from Observations and Interventions. In: Proceedings of the 8th International Conference on Probabilistic Graphical Models (PGM 2016) - JMLR: Workshop and Conference Proceedings 52: . Paper presented at The 8th International Conference on Probabilistic Graphical Models (PGM 2016), Lugano, Switzerland, September 6-9, 2016.
Open this publication in new window or tab >>Learning Acyclic Directed Mixed Graphs from Observations and Interventions
2016 (English)In: Proceedings of the 8th International Conference on Probabilistic Graphical Models (PGM 2016) - JMLR: Workshop and Conference Proceedings 52, 2016Conference paper, Published paper (Refereed)
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-141895 (URN)
Conference
The 8th International Conference on Probabilistic Graphical Models (PGM 2016), Lugano, Switzerland, September 6-9, 2016
Available from: 2017-10-12 Created: 2017-10-12 Last updated: 2018-01-13Bibliographically approved
Pena, J. M. & Gomez-Olmedo, M. (2016). Learning marginal AMP chain graphs under faithfulness revisited. International Journal of Approximate Reasoning, 68, 108-126
Open this publication in new window or tab >>Learning marginal AMP chain graphs under faithfulness revisited
2016 (English)In: International Journal of Approximate Reasoning, ISSN 0888-613X, E-ISSN 1873-4731, Vol. 68, p. 108-126Article in journal (Refereed) Published
Abstract [en]

Marginal AMP chain graphs are a recently introduced family of models that is based on graphs that may have undirected, directed and bidirected edges. They unify and generalize the AMP and the multivariate regression interpretations of chain graphs. In this paper, we present a constraint based algorithm for learning a marginal AMP chain graph from a probability distribution which is faithful to it. We show that the marginal AMP chain graph returned by our algorithm is a distinguished member of its Markov equivalence class. We also show that our algorithm performs well in practice. Finally, we show that the extension of Meeks conjecture to marginal AMP chain graphs does not hold, which compromises the development of efficient and correct score+search learning algorithms under assumptions weaker than faithfulness. (C) 2015 Elsevier Inc. All rights reserved.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE INC, 2016
Keywords
Chain graphs; AMP chain graphs; MVR chain graphs; Structure learning
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-124106 (URN)10.1016/j.ijar.2015.09.004 (DOI)000366774200009 ()
Note

Funding Agencies|Center for Industrial Information Technology [09.01]; Swedish Research Council [2010-4808]; Spanish Ministry of Economy and Competitiveness [TIN2013-46638-C3-2-P]; European Regional Development Fund (FEDER)

Available from: 2016-01-25 Created: 2016-01-19 Last updated: 2018-01-10
Sonntag, D. & Pena, J. M. (2016). On expressiveness of the chain graph interpretations. International Journal of Approximate Reasoning, 68, 91-107
Open this publication in new window or tab >>On expressiveness of the chain graph interpretations
2016 (English)In: International Journal of Approximate Reasoning, ISSN 0888-613X, E-ISSN 1873-4731, Vol. 68, p. 91-107Article in journal (Refereed) Published
Abstract [en]

In this article we study the expressiveness of the different chain graph interpretations. Chain graphs is a class of probabilistic graphical models that can contain two types of edges, representing different types of relationships between the variables in question. Chain graphs is also a superclass of directed acyclic graphs, i.e. Bayesian networks, and can thereby represent systems more accurately than this less expressive class of models. Today there do however exist several different ways of interpreting chain graphs and what conditional independences they encode, giving rise to different so-called chain graph interpretations. Previous research has approximated the number of representable independence models for the Lauritzen-Wermuth-Frydenberg and the multivariate regression chain graph interpretations using an MCMC based approach. In this article we use a similar approach to approximate the number of models representable by the latest chain graph interpretation in research, the Andersson-Madigan-Perlman interpretation. Moreover we summarize and compare the different chain graph interpretations with each other. Our results confirm previous results that directed acyclic graphs only can represent a small fraction of the models representable by chain graphs, even for a low number of nodes. The results also show that the Andersson-Madigan-Perlman and multivariate regression interpretations can represent about the same amount of models and twice the amount of models compared to the Lauritzen-Wermuth-Frydenberg interpretation. However, at the same time almost all models representable by the latter interpretation can only be represented by that interpretation while the former two have a large intersection in terms of representable models. (C) 2015 Elsevier Inc. All rights reserved.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE INC, 2016
Keywords
Chain graphs; Lauritzen-Wermuth-Frydenberg interpretation; Andersson-Madigan-Perlman interpretation; Multivariate regression interpretation; MCMC sampling; Expressibility of probabilistic graphical models
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-124105 (URN)10.1016/j.ijar.2015.07.009 (DOI)000366774200008 ()
Note

Funding Agencies|Swedish Research Council [2010-4808]

Available from: 2016-01-25 Created: 2016-01-19 Last updated: 2018-01-10
Keivani, O. & Peña, J. M. (2016). Uni- and Multi-Dimensional Clustering Via Bayesian Networks. In: Emre Celebi, Kemal Aydin (Ed.), Unsupervised Learning Algorithms: (pp. 163-192). Cham: Springer
Open this publication in new window or tab >>Uni- and Multi-Dimensional Clustering Via Bayesian Networks
2016 (English)In: Unsupervised Learning Algorithms / [ed] Emre Celebi, Kemal Aydin, Cham: Springer, 2016, p. 163-192Chapter in book (Refereed)
Place, publisher, year, edition, pages
Cham: Springer, 2016
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-128190 (URN)9783319242118 (ISBN)
Available from: 2016-05-20 Created: 2016-05-20 Last updated: 2018-01-10Bibliographically approved
Sonntag, D., Peña, J. M. & Gómez-Olmedo, M. (2015). Approximate Counting of Graphical Models Via MCMC Revisited. International Journal of Intelligent Systems, 30(3), 384-420
Open this publication in new window or tab >>Approximate Counting of Graphical Models Via MCMC Revisited
2015 (English)In: International Journal of Intelligent Systems, ISSN 0884-8173, E-ISSN 1098-111X, Vol. 30, no 3, p. 384-420Article in journal (Refereed) Published
Abstract [en]

We apply MCMC sampling to approximately calculate some quantities, and discuss their implications for learning directed and acyclic graphs (DAGs) from data. Specifically, we calculate the approximate ratio of essential graphs (EGs) to DAGs for up to 31 nodes. Our ratios suggest that the average Markov equivalence class is small. We show that a large majority of the classes seem to have a size that is close to the average size. This suggests that one should not expect more than a moderate gain in efficiency when searching the space of EGs instead of the space of DAGs. We also calculate the approximate ratio of connected EGs to connected DAGs, of connected EGs to EGs, and of connected DAGs to DAGs. These new ratios are interesting because, as we will see, they suggest that some conjectures that appear in the literature do not hold. Furthermore, we prove that the latter ratio is asymptotically 1.

Finally, we calculate the approximate ratio of EGs to largest chain graphs for up to 25 nodes. Our ratios suggest that Lauritzen-Wermuth-Frydenberg chain graphs are considerably more expressive than DAGs. We also report similar approximate ratios and conclusions for multivariate regression chain graphs.

Place, publisher, year, edition, pages
Wiley-Blackwell, 2015
Keywords
MCMC sampling, Bayesian networks, Chain graphs, Lauritzen-Wermuth-Frydenberg interpretation, Multivariate regression interpretation
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-105815 (URN)10.1002/int.21704 (DOI)000348308600008 ()
Note

This work is funded by the Center for Industrial Information Technology (CENIIT) and a so-called career contract at Linkoping University, by the Swedish Research Council (ref. 2010-4808), and by FEDER funds and the Spanish Government (MICINN) through the projects TIN2010-20900-C04-03 and TIN2010-20900-C04-01.

Available from: 2014-04-08 Created: 2014-04-08 Last updated: 2018-01-11Bibliographically approved
Sonntag, D. & Pena, J. M. (2015). Chain graph interpretations and their relations revisited. International Journal of Approximate Reasoning, 58, 39-56
Open this publication in new window or tab >>Chain graph interpretations and their relations revisited
2015 (English)In: International Journal of Approximate Reasoning, ISSN 0888-613X, E-ISSN 1873-4731, Vol. 58, p. 39-56Article in journal (Refereed) Published
Abstract [en]

In this paper we study how different theoretical concepts of Bayesian networks have been extended to chain graphs. Today there exist mainly three different interpretations of chain graphs in the literature. These are the Lauritzen-Wermuth-Frydenberg, the Andersson-Madigan-Perlman and the multivariate regression interpretations. The different chain graph interpretations have been studied independently and over time different theoretical concepts have been extended from Bayesian networks to also work for the different chain graph interpretations. This has however led to confusion regarding what concepts exist for what interpretation. In this article we do therefore study some of these concepts and how they have been extended to chain graphs as well as what results have been achieved so far. More importantly we do also identify when the concepts have not been extended and contribute within these areas. Specifically we study the following theoretical concepts: Unique representations of independence models, the split and merging operators, the conditions for when an independence model representable by one chain graph interpretation can be represented by another chain graph interpretation and finally the extension of Meeks conjecture to chain graphs. With our new results we give a coherent overview of how each of these concepts is extended for each of the different chain graph interpretations.

Place, publisher, year, edition, pages
Elsevier, 2015
Keywords
Chain graphs; Lauritzen-Wermuth-Frydenberg interpretation; Andersson-Madigan-Perlman interpretation; Multivariate regression interpretation
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-116826 (URN)10.1016/j.ijar.2014.12.001 (DOI)000350516100004 ()
Note

Funding Agencies|Center for Industrial Information Technology (CENIIT); Swedish Research Council [2010-4808]; FEDER funds; Spanish Government (MICINN) [TIN2010-20900-004-03]

Available from: 2015-04-07 Created: 2015-04-07 Last updated: 2018-01-11
Sonntag, D. & Peña, J. M. (2015). Chain Graphs and Gene Networks. In: Arjen Hommersom and Peter J.F. Lucas (Ed.), Foundations of Biomedical Knowledge Representation: Methods and Applications (pp. 159-178). Springer
Open this publication in new window or tab >>Chain Graphs and Gene Networks
2015 (English)In: Foundations of Biomedical Knowledge Representation: Methods and Applications / [ed] Arjen Hommersom and Peter J.F. Lucas, Springer, 2015, p. 159-178Chapter in book (Refereed)
Abstract [en]

Chain graphs are graphs with possibly directed and undirected edges, and no semidirected cycle. They have been extensively studied as a formalism to represent probabilistic independence models, because they can model symmetric and asymmetric relationships between random variables. This allows chain graphs to represent a wider range of systems than Bayesian networks. This in turn allows for a more correct representation of systems that may contain both causal and non-causal relationships between its variables, like for example biological systems. In this chapter we give an overview of how to use chain graphs and what research exists on them today. We also give examples on how chain graphs can be used to model advanced systems, that are not well understood, such as gene networks.

Place, publisher, year, edition, pages
Springer, 2015
Series
Lecture Notes in Artificial Intelligence, ISSN 0302-9743 ; 9521
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-105813 (URN)10.1007/978-3-319-28007-3_10 (DOI)978-3-319-28006-6 (ISBN)978-3-319-28007-3 (ISBN)
Note

The previous status of this article was Manuscript.

Available from: 2014-04-08 Created: 2014-04-08 Last updated: 2016-03-29Bibliographically approved
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