Sentence examples for assignment of prior from inspiring English sources
Earlier we noted that justifying a Carnapian assignment of prior probabilities only gets us half way to a solution.
We said that mainstream Bayesians regard one assignment of prior probabilities as being as good as any other.
But these precise formulations do not themselves solve a problem like Hume's, for they rely on assumptions like the probability axioms, Carnap's assignment of prior probabilities, and Conditionalization.
But the only prior probability assignment that is invariant in this way is the assignment of prior probability of 1/2 to each of the two possibilities (i.e., that the ball drawn is black or that it is red).
Here we use the most parsimonious approach that does not require assignment of prior distributions to (the unknown) gametocyte densities.
According to mainstream Bayesian epistemology, priors (and sometimes likelihoods) are up for grabs, meaning that one assignment of priors is as good as another, provided both are coherent (that is, they obey the axioms of probability theory).
The important thing for the moment is to appreciate that (i) inductive inference is a dynamic process, since it involves changing our beliefs over time, but (ii) the general probability axioms, and particular assignments of prior probabilities like Carnap's, are static, concerning only the initial probabilities.
For we could then still arrive in a motivated way at our prior probabilities, by proceeding in two steps, namely, by first applying the Principle of Indifference to the partitions separately, thereby possibly obtaining different assignments of priors, and by then taking a weighted average of the thus obtained priors, where the weights, too, are to depend on explanatory considerations.
This manifold structure is problematic for the optimization procedures of the MLE and the assignment of reliable prior distributions in Bayesian analysis.
We especially thank Drs. L. Ziv and H. Baier for help with external sex assignment of fish prior to histological analysis.
Our method allows assignment of different priors to different genes, reflecting their probability to be regulated at the transcription level.
