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Bayesian modeling and analysis has three key components, formally they are described as the prior, the likelihood and the posterior.

Bayes theorem is used to define the posterior in terms of the likelihood (involving the data) and the prior (expressed in terms of the model parameters).

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It is therefore desirable to make a few statements about these procedures that be conveniently under the general term of statistical modelling.

This can be regarded as a conceptual description of the processes that generate the data observed.

Statistics in Practice A new series of practical books outlining the use of statistical techniques in a wide range of application areas: * Human and Biological Sciences * Earth and Environmental Sciences * Industry, Commerce and Finance The authors of this important text explore the processes through which archaeologists analyse their data and how these can be made more rigorous and effective by sound statistical modelling.

They assume relatively little previous statistical or mathematical knowledge.

It may then be possible that some (or all) of the samples can be radiocarbon dated, and then in building the chronology, the C date.

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