| Bayesian methods are seldom seen in the context of plant pathology. However, they offer a number of possibilities in data analysis and decision theory. Most decision makers utilize a Bayes-like methodology to combine prior information with new information, and in this context the acceptance or failure of predictive systems can be itself dependent on prior information. In the context of complex systems, a Bayesian approach to data analysis using MCMC methods offers flexibility beyond that encountered in a frequentist approach. Frequency distributions of parameters become available, and the effect of the certainty of the prior distribution can also be determined |
