Conferences, workshops, courses, and other events

Training

This is a 14-hour course covering the principles of probabilistic modeling using Bayesian networks, building Bayesian networks based on expert knowledge (both structure and numerical parameters), dynamic Bayesian networks, learning Bayesian networks from data and causal discovery, parameter learning, validation techniques, elements of expected utility theory, utility elicitation, and influence diagrams.

 

Meeting times:

The course will take place through on-line meetings (Zoom).

9:00am-11:10am Eastern Time (6:00am-8:10am Pacific Time, 3:00pm-5:10pm Central European Time)

Monday, February 1, 2021

Tuesday, February 2, 2021

Wednesday, February 3, 2021

Thursday, February 4, 2021

Monday, February 8, 2021

Tuesday, February 9, 2021

Wednesday, February 10, 2021

 

Pre-requisites:

Elementary college-level math and computer skills, basic data processing skills through tools such as Excel.  No special prerequisites or knowledge of elements of decision-theoretic modeling or tools such as Bayesian networks.  We will cover all that is required in the course.  While all concepts covered in the course are general, we will use GeNIe to illustrate them.  Tuition covers a 30-day GeNIe license for use during the course.

 

Tuition fee:

Course tuition fee $500 ($300 for students)

There is a minimum of 5 and a maximum of 20 participants.

 

For more information/to register:

Contact training@bayesfusion.com

Training

This is a 12-hour course covering the principles of probabilistic modeling using Bayesian networks, building Bayesian networks based on expert knowledge (both structure and numerical parameters), dynamic Bayesian networks, learning Bayesian networks from data and causal discovery, parameter learning, validation techniques, elements of expected utility theory, utility elicitation, and influence diagrams.

 

Meeting times:

The course will take place through on-line meetings (Zoom).

1:00pm-3:10pm Eastern Time (10am-12:10pm Pacific Time)

Thursday, November 5, 2020

Friday, November 6, 2020

Monday, November 9, 2020

Tuesday, November 10, 2020

Thursday, November 12, 2020

Friday, November 13, 2020

 

Pre-requisites:

Elementary college-level math and computer skills, basic data processing skills through tools such as Excel.  No special prerequisites or knowledge of elements of decision-theoretic modeling or tools such as Bayesian networks.  We will cover all that is required in the course.  While all concepts covered in the course are general, we will use GeNIe to illustrate them.  Tuition covers a 30-day GeNIe license for use during the course.

 

Tuition fee:

Course tuition fee $500 ($300 for students)

There is a minimum of 5 and a maximum of 20 participants.

 

For more information/to register:

Contact training@bayesfusion.com

The Program Committee of the 10th Probabilistic Graphical Models (PGM 2020) conference announced the winner of the BayesFusion Best Student Paper Award in Aalborg, Denmark, on September 25. The winner is:

Alessandro Bregoli, Universita degli Studi di Milano-Bicocca, Milano, Italy, for the paper entitled Constraint-Based Learning for Continuous-Time Bayesian Networks, co-authored with Marco Scutari and Fabio Stella.

The Program Committee of the 9th Probabilistic Graphical Models (PGM-2018) conference announced the winners of the BayesFusion Best Student Paper Award during the PGM-2018 conference banquet in Prague, Czech Republic, on September 13.  The winners are:

BayesFusion Best Student Paper Award given jointly to:

Irene Córdoba, Department of Artificial Intelligence Universidad Politécnica de Madrid, Spain, for the paper entitled A Partial Orthogonalization Method for Simulating Covariance and Concentration Graph Matrices, co-authored with Gherardo Varando, Concha Bielza and Pedro Larrañaga

Kari Rantanen, HIIT, Department of Computer Science, University of Helsinki, Finland, for the paper entitled Learning Optimal Causal Graphs with Exact Search, co-authored with Antti Hyttinen and Matti Järvisalo

Honorable Mentions:
Gherardo Varando, Department of Artificial Intelligence Universidad Politécnica de Madrid, Spain, and Department of Mathematical Sciences, University of Copenhagen, Denmark, for being a student co-author of the paper that won the BayesFusion Best Student Paper Award, entitled A Partial Orthogonalization Method for Simulating Covariance and Concentration Graph Matrices, with Irene Córdoba, Concha Bielza and Pedro Larrañaga

Janne Leppä-aho, University of Helsinki, Department of Computer Science / HIIT, Finland, for the paper entitled Learning Non-parametric Markov Networks with Mutual Information, co-authored with Santeri Räisänen, Xiao Yang and Teemu Roos

BayesFusion, LLC, has offered a $1,000 cash award for the best student paper in the 9th Probabilistic Graphical Models (PGM-2018) conference, which will take place in Prague, Czech Republic, September 11-14.

PyData Warsaw 2017 logo

Prof. Druzdzel, a co-founder of BayesFusion, LLC, gave a presentation on the topic of Learning Bayesian Networks and Causal Discovery at the PyData Warsaw meeting #9: Deep & Machine Learning on 29 March 2017.  A recording of the presentation is available through BayesFusion’s YouTube channel (https://youtu.be/UWvxeOo91Fo).

ABNMS logo

Prof. Druzdzel, a co-founder of BayesFusion, LLC, delivered an invited plenary talk at the Eighth Annual Conference of the Australasian Bayesian Network Modelling Society (ABNMS2016) held at the University of Western Australia on 22-25th November 2016. The conference is an annual meeting of the Australasian Bayesian Network Modelling Society (ABNMS, http://www.abnms.org/). ABNMS’s purpose is to promote the understanding and use of Bayesian network models in scientific, industrial, and research applications. The invited plenary talk, titled “(The Importance of) Human Interface to Bayesian Networks” focused on various user interface techniques employed in GeNIe that compose its widely known user interface. A recording of the talk is available through BayesFusion’s YouTube channel (https://youtu.be/mxeiJbo8FWw).

 

PGM2016 logo

Prof. Druzdzel, one of the co-founders of BayesFusion, LLC, participated in the Eighth International Conference on Probabilistic Graphical Models (PGM), which took place in Lugano, Switzerland, September 6-9, 2016. PGM is a biennial meeting that brings together researchers interested in all aspects of graphical models for probabilistic reasoning, decision making, and learning. Prof. Druzdzel took part in a round-table panel “Probabilistic graphical models, software tools, and their applications to real-world problems.” The panel, whose members included representatives of leading software companies in the field, focused on issues related to practical application of probabilistic graphical models.