A Bayesian State Space Approach to Estimating Pest Management Models

Models for pest management need to incorporate information from many subject areas, including biology, agronomy and economics. The information sources are unrelated except by the structural assumptions of the bio-economic model that is used. Rigorous model estimation is often at the expense of ignoring much of the available information. This paper illustrates how a Bayesian approach using the Gibbs sampler can allow the incorporation of potentially valuable prior information. This approach provides posterior distributions for the important model relationships, giving an indication of confidence in the model and providing a natural starting point for stochastic decision making.


Issue Date:
1995-02
Publication Type:
Conference Paper/ Presentation
Record Identifier:
http://ageconsearch.umn.edu/record/170820
PURL Identifier:
http://purl.umn.edu/170820
Total Pages:
16




 Record created 2017-04-01, last modified 2018-01-22

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