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In agriculture Markov decision processes (MDPs) with finite state and action space are often used to model sequential decision making over time. For instance, states in the process represent possible levels of traits of the animal and transition probabilities are based on biological models estimated from data collected from the animal or herd. State space models (SSMs) are a general tool for modeling repeated measurements over time where the model parameters can evolve dynamically. In this paper we consider methods for embedding an SSM into an MDP with finite state and action space. Different ways of discretizing an SSM are discussed and methods for reducing the state space of the MDP are presented. An example from dairy production is given. [ABSTRACT FROM AUTHOR]

This paper describes the $${{\tt StatWeave}}$$ system for making reproducible statistical analyses. $${{\tt StatWeave}}$$ differs from other systems for reproducible analysis in several ways. The two main differences are: (1) Several statistics programs can be in used in the same document. (2) Documents can be prepared using OpenOffice or $${\hbox{\LaTeX}}$$. The main part of this paper is an example showing how to use $${{\tt R}}$$ and $${{\tt SAS}}$$ together in an OpenOffice text document. The paper also contains some practical considerations on the use of literate programming in statistics. [ABSTRACT FROM AUTHOR]

Hourly pedometer counts and irregularly measured concentration of the hormone progesterone were available for a large number of dairy cattle. A hidden semi-Markov was applied to this bivariate time-series data for the purposes of monitoring the reproductive status of cattle. In particular, the ability to identify oestrus is investigated as this is of great importance to farm management. Progesterone concentration is a more accurate but more expensive method than pedometer counts, and we evaluate the added benefits of a model that includes this variable. The resulting model is biologically sensible, but validation is difficult. We utilize some auxiliary data to demonstrate the model's performance. [ABSTRACT FROM AUTHOR]