  It is recognised that the Bayesian approach to inference can not adequately
cope with all the types of pre-data beliefs about population quantities of
interest that are commonly held in practice. In particular, it generally
encounters difficulty when there is a lack of such beliefs over some or all the
parameters of a model, or within certain partitions of the parameter space
concerned. To address this issue, a fairly comprehensive theory of inference is
put forward called integrated organic inference that is based on a fusion of
Fisherian and Bayesian reasoning. Depending on the pre-data knowledge that is
held about any given model parameter, inferences are made about the parameter
conditional on all other parameters using one of three methods of inference,
namely organic fiducial inference, bispatial inference and Bayesian inference.
The full conditional post-data densities that result from doing this are then
combined using a framework that allows a joint post-data density for all the
parameters to be sensibly formed without requiring these full conditional
densities to be compatible. Various examples of the application of this theory
are presented. Finally, the theory is defended against possible criticisms
partially in terms of what was previously defined as generalised subjective
probability.
