Alternatives to Lavine's algorithm for calculation of posterior bounds given convex sets of distributions
نویسنده
چکیده
This paper presents alternatives to Lavine's algorithm, currently the most popular method for calculation of expectation bounds induced by sets of probability distributions. The White-Snow algorithm is rst analyzed and demonstrated to be superior to Lavine's algorithm in a variety of situations. The calculation of posterior bounds is then reduced to a fractional programming problem. From the unifying perspective of fractional programming, Lavine's algorithm is identical to Dinkelbach's algorithm, and the White-Snow algorithm is essentially identical to the Charnes-Cooper transformation. A novel algorithm for expectation bounds is given for the situation where both prior and likelihood functions are speci ed as convex sets of distributions.
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