cgf.ztrunc

parameter associated with linear combination

a logical value; if TRUE, probabilities are given as $log(p)$.


Given parameters, a normalizing constant for Bickis and Lee's probability distribution is computed.


A collection of tools for conducting an imprecise inference is provided.   Imprecise prior is used for this inference, and imprecise probability theory introduced by Peter Walley (1991) is its underlying theoretical foundation.   The package is developed based on the PhD thesis work of Lee (2014).  Poisson and zero-truncated Poisson sampling models are mainly studied with two types of prior distributions.

cgf: Comupting Normalizing Constant of Bickis and Lee's Probability Distribution

Description

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