Webinar: Introduction to BEKEE, the Bayesia Expert Knowledge Elicitation Environment
Thursday, June 7, 2012, 12 noon (CDT, GMT -05:00)
Online Tutorial Hosted by Lionel Jouffe and Stefan Conrady
Everybody is talking about "Big Data" and all the manifold opportunities that are associated with it. Very often though, we hear almost as much about the challenges that come with this flood of data. Where to store it, how to analyze it, how to explain it, the list goes on and on. We think this is a very nice problem to have. Much more serious problems exist on the opposite end of the spectrum, where there is not enough data. Unfortunately, all the advanced knowledge discovery algorithms fail in the absence of data.
Read more: Webinar: Introduction to BEKEE, the Bayesia Expert Knowledge Elicitation Environment
A Visual Tour of BayesiaLab
This 5-minute video highlights the key functionalities of the BayesiaLab 5.0 software package and its highly intuitive user interface. The demo will present how Bayesian networks can be built manually from expert knowledge, plus a knowledge discovery algorithm is shown that automatically learns a network structure from data.
BayesiaLab 5.0: Analytics, Data Mining, Modeling & Simulation
BayesiaLab is a powerful desktop application (Windows/Mac/Unix) for knowledge discovery, data mining, analytics, predictive modeling and simulation - all based on the paradigm of Bayesian networks. Bayesian networks have become a very powerful tool for deep understanding of very complex, high-dimensional problem domains, ranging from bioinformatics to marketing science.
Read more: BayesiaLab 5.0: Analytics, Data Mining, Modeling & Simulation
Bayesia Market Simulator
BayesiaLab and Bayesia Market Simulator are unique in their ability to use Bayesian networks for choice modeling and market share simulation. Together they provide a comprehensive toolset for simulating market shares of future products based on their key characteristics, without requiring new and costly experiments. As a result, BayesiaLab and Bayesia Market Simulator allow using a vast range of existing research for market share predictions.
Missing Values Imputation with Bayesian Networks
With the abundance of “big data” in the field of analytics, and all the challenges today’s immense data volume is causing, it may not be particularly fashionable or pressing to discuss missing values. After all, who cares about missing data points when there are petabytes of more observations out there?
As missing values processing (beyond the naïve ad-hoc approaches) can be a demanding task, both methodologically and computationally, the principal objective of this paper is to propose a new and hopefully easier approach by employing Bayesian networks. It is not our intention to open the proverbial “new can of worms”, and thus distract researchers from their principal study focus, but rather we want to demonstrate that Bayesian networks can reliably, efficiently and intuitively integrate missing values processing into the main research task.
#16 of 100 things you need to know about Bayesian networks
Bayesian Networks as Framework for Probabilistic Reasoning
One of the principal motivations for employing Bayesian networks is probabilistic reasoning. In his new book, David Barber concisley summarizes this concept as follows:
"The central paradigm of probabilistic reasoning is to identify all relevant variables x1, . . . , xN in the environment [i.e. the domain under study], and make a probabilistic model p(x1, . . . , xN) of their interaction [i.e. represent the variables' joint probability distribution]. Reasoning (inference) is then performed by introducing evidence that sets variables in known states, and subsequently computing probabilities of interest, conditioned on this evidence. The rules of probability, combined with Bayes' rule make for a complete reasoning system, one which includes traditional deductive logic as a special case."[1]
Read more: #16 of 100 things you need to know about Bayesian networks
#15 of 100 things you need to know about Bayesian networks
Learning = Data Compression
"It has long been understood that even when confronted with a ten-gigabyte file containing data to be statistically analyzed, the actual information-theoretic amount of information in the file might be much less, per haps merely a few hundred megabytes. This insight is currently most commonly used by data analysts to take high-dimensional real-valued datasets and reduce their dimensionality using principal components analysis, with little loss of meaningful information. This can turn an apparently intractably large data mining problem into an easy problem." [1]
Read more: #15 of 100 things you need to know about Bayesian networks
#14 of 100 things you need to know about Bayesian networks
Dealing with Multicollinearity
Compared to standard regression models in which the correlation between the variables leads to multicollinearity and lack of robustness of model fitting, Bayesian networks leverage the mutual correlation between variables to define the conditional probability distributions. These conditional distributions become the "bricks" used to build complex systems in a modular way. Specifically, the modularity provides the critical advantage of breaking down the discovery process into the search for the specific components of a complex model.
Read more: #14 of 100 things you need to know about Bayesian networks
