Preface


The Bayesian Modeling Applications Workshop (BMAW) has been held in con-
junction with the annual Conference on Uncertainty in Artificial Intelligence
thirteen times since 2003. The workshop brings together researchers and practi-
tioners who apply the technologies pioneered by the UAI community to address
important real-world problems in a diverse set of fields. The workshop fosters
discussion on the challenges of building applications, such as understanding and
addressing stakeholder needs; integrating Bayesian models and tools into larger
applications; validating models; interacting with users; construction of models
through knowledge elicitation and learning; agile model and system development
strategies; and deploying and managing Web based Bayesian applications.
    The theme of the Workshop has adapted from year to year, as real-world
problems change and technologies evolve to meet them. The frenzy to apply con-
ventional machine learning methods for commercial applications has the danger
of overwhelming Bayesian methods where they might be best applied. Bayesian
methods face a similar challenge to the one they faced a decade ago by this
community: To demonstrate their timeliness in the current environment of in-
telligent systems and a long tail of related decision and prediction tasks. This
Workshop demonstrates that through several tools and current applications of
Bayesian methods.
    A call for papers encouraged submissions in a variety of domains, but not
limited to any specific vertical market or discipline. Submissions were expected to
foster discussion of critical issues within the community of practice. There were 9
submissions. Each submission was reviewed by at least three program committee
members. Eight papers were accepted and presented at the Workshop. Seven of
these appear full length in these proceedings. One appears as extended abstract
to facilitate future publication. In addition, three invited speakers have blessed
the Workshop with the presentation of their poster paper accepted at the main
conference.
    The Thirteenth Annual BMAW was held on June 25, 2016, in New York City,
NY, USA. About 30 people attended the Workshop, which consisted of eleven
paper presentations, questions, and the accompanying discussions. Papers and
presentations addressed Bayesian learning algorithms, tools, and several appli-
cations involving medical, government, tax, robotics, soccer, corruption, and
education domains. We are grateful to the paper authors and presenters for
their contributions, and to the program committee members for their careful
e↵orts reviewing and commenting on submissions. We also appreciate the help
EasyChair has always provided us and the organizational support provided by
the UAI conference organizers, without whom the workshop would not be possi-
ble. Finally, we also thank the authors of the main conference for accepting our
invitation to present an invited talk.



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June 2016                      Rommel Novaes Carvalho
New York City, NY, USA        Kathryn Blackmond Laskey
                                    Workshop Co-Chairs




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Program Committee

Russell Almond       Florida State University
Rommel Carvalho      University of Brası́lia / Brazil’s Office of the
                     Comptroller General
Feng Chen            SUNY Albany
Paulo Costa          George Mason University
Pablo González      Instituto de Investigaciones Electricas Mexico
Sajjad Haider        Institute of Business Administration
Arjen Hommersom      Open University of the Netherlands
Oscar Kipersztok     The Boeing Company
Helge Langseth       Norwegian University of Science and Technology
Kathryn Laskey       George Mason University
Ole Mengshoel        Carnegie Mellon University
Tomas Singliar       Microsoft
V Anne Smith         University of St Andrews
Luis Enrique Sucar   INAOE




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