Message-ID: <1131542484.18450.1568623581510.JavaMail.confluence@confluence6> Subject: Exported From Confluence MIME-Version: 1.0 Content-Type: multipart/related; boundary="----=_Part_18449_490635296.1568623581509" ------=_Part_18449_490635296.1568623581509 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Content-Location: file:///C:/exported.html Knowledge Modeling and Probabilistic Reasoning with Bayesian Net= works

# Motivation ## Objective of Tut= orial

The objective of this tutorial is to introduce you to knowledge mo= deling and omnidirectional probabilistic inference with Bayesian Networks, = using the BayesiaLab software platform. In this context, you will learn abo= ut several basic properties of Bayesian Networks and how to apply them in p= ractice.

We recommend this tutorial as starting point for learning about Ba= yesian Networks in general. Given the small size of the problem, you can qu= ickly move back and forth between knowledge modeling and perform- ing infer= ence. This should help you to develop an intuitive understanding of the ben= efits of reasoning with Bayesian Networks. Hopefully, you will find that Ba= yesian Networks make formal reasoning as straightforward as doing arithmeti= c with a spreadsheet.

As opposed to previous BayesiaLab tutorials, which involved machin= e learning from data for generating Bayesian Networks, we will now exclusiv= ely assemble a network using domain knowledge, including a num- ber of caus= al assumptions, and uncertainty. More formally, we will encode propositiona= l, associational, and causal knowledge in a Bayesian Network.

The focus on knowledge modeling in this paper is meant to highligh= t that Bayesian Networks can work with the entire spectrum of knowledge sou= rces, from no data to big data. =

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