Practical Bayesian work is based on probability models for the data. In most real-world problems there is uncertainty about the correct model to use, and more than one model is typically under consideration. I will introduce the basics of Bayesian model inference, including model comparison and model averaging. Methods for implementing Bayesian model inference will be briefly discussed. I will also raise concerns about Bayesian model inference, and criticise the way this theory is used in a large portion of practical applications.As last year the conference will also include a number of short contributed talks. New for this year is that in connection to the conference there will also be a hands on session which will introduce Bayesian Hierarchical Modeling.
Bayes@Lund 2015 – a Mini-conference on Bayesian Methods at Lund University, 10th of February, 2015
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Published 15 January 2015
Following <a href="http://www.lucs.lu.se/bayes-at-lund-2014/">the success from last year</a> we are happy to present the sequel: Bayes@Lund 2015. The purpose of this conference is to bring together researchers working with or interested in Bayesian methods.
Following the success from last year we are happy to present the sequel: Bayes@Lund 2015. The purpose of this conference is to bring together researchers working with or interested in Bayesian methods. The focus will be on how Bayesian methods are used in research, what advantages Bayesian methods have over classical alternatives, and how the use and teaching of Bayesian methods can be encouraged.
[caption id="attachment_5357" align="alignleft" width="120" caption="Mattias Villani, Linköping University."]http://www.lucs.lu.se/wp-content/uploads/2014/11/mattias_villani.jpeg[/caption]
This year we are happy to announce that the invited speaker is Mattias Villani. He is Professor of Statistics at the Division of Statistics and Machine Learning at the Department of Computer and Information Science (IDA), Linköping University. His research focuses on developing Bayesian methods for inference and prediction using flexible models. Mattias is going to talk about: Bayesian model inference - why, what and how?
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