Hoping someone might be able to provide a “cheat sheet” for effective selection of prior distributions, e.g. a list of available distributions with accompanying sentence explaining when each should be used. Or, alternatively, recommend a good resource. I have some general questions but also a specific model I am trying to specify. General questions:

- When should HalfNormal be chosen instead of HalfCauchy? Why would you choose one over the other?
- How might one choose between Normal, Gamma, Exponential and Poission prior distributions?
- I understand it is desirable for all random variables in the statistical model to have similar magnitude. If this is not possible for a particular parameter, modelling the logarithm of the parameter can be effective. Are there other ways of achieving this if using the logarithm is not suitable?

The model I am trying to specify is the following:

I have data for H at four time values and wish to estimate the five parameters B, C, D, t_2, H_{ult}. I am using a continuous switch point for the change of curve as seen in Model with conditional parameters causing mass matrix to contain zeros on diagonal.

- All parameters are positive values. Is it therefore effective to use HalfNormal or HalfCauchy prior distributions?
- As t_2 lies somewhere between 0 and t_{end}, is it effective to use the Beta prior distribution with \alpha=1 and \beta=1 and scale it up by t_{end}?
- Should C have a special prior distribution as it is an exponent?