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Bayesian networks for discrete multivariate data: an algebraic approach to inference

✍ Scribed by J.Q. Smith; J. Croft

Publisher
Elsevier Science
Year
2003
Tongue
English
Weight
213 KB
Volume
84
Category
Article
ISSN
0047-259X

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✦ Synopsis

In this paper we demonstrate how Gro¨bner bases and other algebraic techniques can be used to explore the geometry of the probability space of Bayesian networks with hidden variables. These techniques employ a parametrisation of Bayesian network by moments rather than conditional probabilities. We show that whilst Gro¨bner bases help to explain the local geometry of these spaces a complimentary analysis, modelling the positivity of probabilities, enhances and completes the geometrical picture. We report some recent geometrical results in this area and discuss a possible general methodology for the analyses of such problems.

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Bayesian inference for the mover–stayer
Bayesian inference for the mover–stayer model in continuous time with an application to labour market transition data
✍ Denis Fougère; Thierry Kamionka 📂 Article 📅 2003 🏛 John Wiley and Sons 🌐 English ⚖ 318 KB

## Abstract This paper presents Bayesian inference procedures for the continuous time mover–stayer model applied to labour market transition data collected in discrete time. These methods allow us to derive the probability of embeddability of the discrete‐time modelling with the continuous‐time one