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Introduction to Bayesian Networks for risk and impact assessment to support decision making

Seminar
This workshop aims to introduce Bayesian (Belief) Networks to students and researchers. We will provide a theoretical background together with hands on exercises from risk and impact assessments and decision making.

Target audience:

Undergraduate and graduate students and researchers interested in learning how to build and use Bayesian (Belief) Networks to integrate evidence, assess risk or impacts, and evaluate decision alternatives. We will use examples from environmental risk and impact assessments, but the scope of the workshop is BNs with no particular type of application. No previous experience in BNs is needed. Participants are encouraged to study a literature list sent out prior to the workshop.

Motivation:

A Bayesian Network is a modelling framework that enable us to integrate evidence to inform decisions, based on causal relations between decision, state and impact variables. BNs allows integration of data and expert knowledge.

The workshop will be facilitated by:

Wayne Landis, Western Washington University, United States.

Wayne is a Professor in Environmental Science and a Director of the Institute of Environmental Toxicology at Western Washington University. He has over 20 years of experience in ecological risk assessment research, using Bayesian networks to guide decision making.

Ullrika Sahlin, Lund University Center for Environmental and Climate Research, Lund, Sweden.

Ullrika is a researcher in Environmental Science at Lund University. She is studying the treatment of uncertainty in risk assessment and evidence based decision making, and has during the last five years been working with promoting Bayesian approaches in research.

More up-to-date information is available here

The workshop is free of charge and the number of places are limited.

Sign up here. (limited seats)

The workshop is supported by the research school ClimBEco.

Time: 
28/03/2017 (All day)

About the event

Time: 
28/03/2017 (All day)