**Recent Clients**

- BayesiaLab Course Videos
**2018 Event Calendar**- Jan. 9-11, 2018

Introductory Course in Washington, D.C. - Jan. 16, 2018

Seminar in Washington, D.C.: Knowledge Discovery with Bayesian Networks & VR - Jan. 17, 2018

Seminar in Philadelphia: Knowledge Discovery with Bayesian Networks & VR - Jan. 18, 2018

Seminar in New York: Knowledge Discovery with Bayesian Networks & VR - Jan. 19, 2018

Seminar in Boston: Knowledge Discovery with Bayesian Networks & VR - Jan. 22, 2018

Seminar in Montreal: Knowledge Discovery with Bayesian Networks & VR - Jan. 23, 2018

Seminar in Ottawa: Knowledge Discovery with Bayesian Networks & VR - Jan. 24, 2018

Seminar in Toronto: Knowledge Discovery with Bayesian Networks & VR - Jan. 25, 2018

Seminar in Dearborn: Knowledge Discovery with Bayesian Networks & VR - Feb. 6-8, 2018

Introductory Course in London, UK - Feb. 28-Mar. 2, 2018

Introductory Course in Dubai, UAE - Mar. 13-15, 2018

Introductory Course in San Francisco - May 16-18, 2018

Introductory Course in Seattle, WA - May 21-23, 2018

Advanced Course in Seattle, WA - June 26-28, 2018

Introductory Course in Boston - Sep. 26-28, 2018

Introductory Course in New Delhi - Oct. 29-31

Introductory Course in Chicago - Nov. 1-2, 2018

BayesiaLab Conference in Chicago - Nov. 5-7, 2018

Advanced Course in Chicago - Call for Presentations
- Review of the 2014 BayesiaLab Conference
- Review of the 2015 BayesiaLab Conference
**David Aebischer:**

Using Bayesian Networks to Optimize Specific Fuel Consumption**Roman Fomin:**

Innovations in Managing the Training Process in Elite Sports**Dominique Haughton:**

Causal Business Analytics**Neeraj Kulkarni:**

Forecasting and Decision Making Under Uncertainty**Larry Price:**

Learning Dynamic Bayesian Networks from fMRI Time Series**Swapnil Rajiwade & Michael Abramovich:**

A Case Study Guide to Avoid Bayesian Network Modeling Pitfalls**Azizi Seixas:**

Using Machine Learning to Determine effects of Sleep Duration and Physical Activity on Stroke Risk**Steve Wilson:**

Big data, small data: Bayesian networks in environmental policy analysis in Canada’s energy sector

- Review of the 2016 BayesiaLab Conference
**Roman Fomin**

Modeling Athletes' Training Process**Boris Sobolev**

Causal Attribution of Mortality to Delays in Heart Surgery**Aebischer, Tatman, Hepler, & Tractenberg**

Engineering Knowledge for Bayesian Networks**Charles Hammerslough**

Using Bayes Networks to Estimate Return on Marketing Investment**Steven Wilson**

Use of causal modeling with Bayesian networks to inform policy options for sustainable resource management**Bob Wood & Toshi Yumoto**

Using Bayes Nets for Market Share Driver Analyses**Cory Hutchinson**

Analyzing Injury Levels with Bayesian Networks**Sri Srikanth & Corey Sykes**

Driving Digital Customer Engagement Powered by Bayesian Networks**Floyd Demmon**

Supervised Learning to Understand Root Causes of Steelmaking Slivers Using BayesiaLab**Benoit Hubert**

Optimization of Real-Time Experience Measurement with Bayesian Networks**Asim Zia**

Machine learning how human risk perceptions shape behavior**Neeraj Kulkarni**

Demystifying the Consumer Decision Journey

- Review of the 2017 BayesiaLab Conference
**The Conference Agenda****Gabriel Andraos**

Workflow automation in Bayesialab with applications to time series analysis**Debashish Banerjee**

BBN modeling – machine learning derivatives in FMCG, hot rolling mills, polymer extrusion, paper and corrugation industry**Debashish Banerjee**

BBN modeling in predicting cancer using AURA analysis**Olivier Cussenot**

Bayesian Networks and Integrative Semiotic Models in Precision Medicine**Ajith Govind**

Credit Card Fraud and Anomaly Detection using Bayesian and Neural Networks**Bart Jansen**

Stroke Triage with Limited Information**Joanna Jaworska**

Bayesian integrated testing strategy (ITS) for skin sensitization potency assessment**Jacqueline MacDonald Gibson**

Bayesian Network Models for Predicting Health Risks of Arsenic in Drinking Water**Christophe Simon**

Modelling epistemic and aleatory uncertainty in Bayesian network for dependability analysis**Marie Thomas**

Evaluating the link between microbiome and cosmetic clinical signs with PLS and Bayesian approaches**Alta de Waal**

Spatially Discrete Probability Maps for Anti-Poaching Efforts**Philippe Weber**

Bayesian Networks Application to the Dependability and the Control of Dynamic Systems**Steven Wilson**

The Social Graph: Using Bayesian Networks to Identify Spatial Population Structuring Among Caribou in Subarctic Canada

## Knowledge Discovery with

Bayesian Networks and Virtual Reality

**Tuesday, January 16, 2018, 1:00 p.m. – 4:00 p.m.****University of Phoenix, 25 Massachusetts Ave NW, Classroom 105, Washington, DC 20001**

Many of today's popular machine-learning techniques produce black-box models. While such models can be extremely powerful in terms of their predictive performance, they often turn out to be useless for the structural understanding of their underlying problem domains. Thus, questions about why and how something is happening can rarely be answered with such models. Most importantly, causal questions are entirely out of scope for even the most advanced Artificial Intelligence methods.

In this seminar, we pursue a different approach and perform machine learning with the objective of discovering new knowledge from data.

For this purpose, we present Bayesian networks as a type of Artificial Intelligence that can help explore complex problems. We introduce the remarkably simple theory behind Bayesian networks and how it relates to probability calculus and statistics.

Furthermore, we use BayesiaLab's machine-learning algorithms to produce meaningful and easily interpretable graphical models of complex domains with hundreds and even thousands of variables. We go from raw data to very explicit, high-dimensional models within seconds.

In the seminar, we showcase examples from different fields of study, including finance, biology, and economics and employ BayesiaLab's supervised and unsupervised learning algorithms. We can directly compare the machine-learned graphs to our background knowledge.

However, a new challenge emerges at this point. It is no longer the lack of explicitness that hinders human comprehension, it is the opposite. The multitude of simultaneous relationships leads to cognitive overload when complex graphical models are flattened for display on screen or paper.

Fortunately, recent advances in Virtual Reality have opened up new opportunities for overcoming the constraints of two dimensions. With the recent launch of BayesiaLab 7, we can now leverage Virtual Reality methods to visualize Bayesian networks in three dimensions. The depth of space literally allows untangling complex Bayesian network graphs. Our natural cognitive ability can now capture the richness of relationships represented in models. Needless to say, this approach facilitates the exploration of large and complex problem domains, which were practically impossible to comprehend in the past.

Seminar participants will have the opportunity to try out BayesiaLab's VR module using the Oculus Rift during the last hour of the seminar. This VR module is available as a free download for all users of BayesiaLab 7 Professional.

## Seminar Overview

- Big Data & Artificial Intelligence, their promise and their limitations for research
- Map of Analytic Modeling
- Purpose of Models: Prediction vs. Explanation
- Source of Models: Data vs. Theory

- Why Bayesian Networks?
- Introductory Example: Differential Diagnosis of Diseases
- Joint Probability Distribution
- Inference through conditioning and marginalizing
- Independence assumptions from domain knowledge
- Direct encoding of causal knowledge into a Bayesian network

- Properties of Bayesian Networks
- Compact representation of the joint probability distribution
- No distinction between dependent and independent variables
- Omnidirectional inference
- Non-parametric & probabilistic
- Causal

- What is BayesiaLab?
- Supervised Learning for Classification
- Learning = Searching
- Minimum Description Length as a heuristic for network learning
- Information-theoretic measures: Entropy, Mutual Information, Kullback-Leibler Divergence
- Examples:
- The Wisconsin Breast Cancer Database
- The Cancer Genome Atlas

- Unsupervised Learning for Knowledge Discovery
- Examples:
- S&P 500 Ticker Data
- New Vehicle Experience Survey, including 1,000 variables consisting of product features, consumer ratings, demographics, and psychographics
- Introducing the new Multinet Data Clustering algorithm for discovering behavioral segments among consumers.

- Examples:
- Virtual Reality Demo with the Oculus Rift
- 3D exploration of Bayesian networks
- Examples:
- Database of 10-K filings of public companies
- National Health and Nutrition Examination Survey
- Federal Crash Databases (FARS, NASS, LTCCS)

## Who should attend?

Biostatisticians, clinical scientists, data scientists, decision scientists, demographers, ecologists, econometricians, economists, epidemiologists, knowledge managers, management scientists, market researchers, marketing scientists, operations research analysts, policy analysts, predictive modelers, research investigators, risk managers, social scientists, statisticians, plus students and teachers of related fields.

**Please note that this seminar is geared towards applied researchers, NOT software developers or computer scientists. Questions related to algorithms, programming, scalability, architecture, infrastructure, etc., will be out of scope at this event.**

## About the Presenter

Stefan Conrady has over 15 years of experience in decision analysis, market research, and product strategy with Fortune 100 companies in North America, Europe, and Asia. Today, in his role as Managing Partner of Bayesia USA and Bayesia Singapore, he is recognized as a thought leader in applying Bayesian networks for research, analytics, and reasoning. In this context, Stefan has recently co-authored a book, Bayesian Networks & BayesiaLab - A Practical Introduction for Researchers.