=Paper=
{{Paper
|id=None
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1413/invited1.pdf
|volume=Vol-1413
}}
==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1413/invited1.pdf</pdf>
<pre>
BIMS: for Bayesian inference of model structure

                              Nicos Angelopoulos

 Department of Surgery and Cancer, Division of Cancer, Imperial College London,
                 Hammersmith Hospital Campus, London, UK.

    MCMC methods in probabilistic logic programming settings are gaining pop-
ularity and a number of different approaches have been proposed recently. We
discuss theoretical results and experiences with applications of one of the first
approaches in the field. The knowledge representation capabilities of the under-
lying language, which are less well documented in the literature, are discussed,
as well as the machine learning applications of the general framework, which
have been presented in a number of publications. We focus on how to express
Bayesian prior knowledge in this formalism, and show how it can be used to
define generative priors over statistical model spaces: Bayesian networks and
classification and regression trees. Finally, we discuss a Metropolis-Hastings al-
gorithm that can take advantage of the defined priors and its application to
real-world machine learning tasks. Details of the associated publicly available
software are also discussed.




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</pre>