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
Bayesian Networks in the Cloud
ProductsHow to use AWS Lambda with SMILE and more – see our new video:
Maven repository for jSMILE
ProductsBayesFusion’s Maven repository for jSMILE is now available. If you use jSMILE in a Maven-based project, you can reference the library directly in your POM file. For more details (including native library integration in POM), please refer to the Platforms and Wrappers/Java and jSMILE /Maven section in SMILE Wrappers Programmer’s Manual at our documentation website:
https://support.bayesfusion.com/docs/
SMILE Running on ESP32
ProductsSMILE library running on an ESP32 microcontroller
BayesFusion On-Line Course on “Decision-theoretic Modeling” Feb 1-10, 2021
Business, EventsThis 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
BayesFusion On-Line Course on “Decision-theoretic Modeling” Nov 5-13, 2020
Business, EventsThis 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
PGM-2020 BayesFusion Best Student Paper Award winner announced
EventsThe 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.
SMILE 1.6 Released
ProductsBayesFusion releases SMILE 1.6. This version fully supports Unicode in node identifiers, names, and other textual attributes stored in models.
To download the library, visit https://download.bayesfusion.com
The documentation is available at https://support.bayesfusion.com/docs
GeNIe 3.0 released
ProductsWe have just released GeNIe 3.0. This version includes a user interface refresh and full support for Unicode in directory paths and all texts inside models, so GeNIe 3.0 supports non-Latin alphabets.
To download the GeNIe Installer, visit https://download.bayesfusion.com
GeNIe 2.5 released
ProductsWe have released GeNIe 2.5. This version includes QGeNIe, a qualitative interface to Bayesian networks useful for rapid prototyping and support of strategic decisions in group settings.
We have also introduced many small improvements that further enhance user modeling interface, including streamlining GeNIe diagnostic interface, various ways of selecting and highlighting model elements, and model navigation.
BayesBox Tutorial Video
ProductsBayesBox is an online viewer/repository for Bayesian networks and other probabilistic graphical model types.