We used six rhizobium-legume systems to test the reliability of the most-probable-number (MPN) technique for enumerating rhizobia introduced into 14 sites representing four soil orders. The range-of-transition values (the number of dilution steps between the first not-entirely-positive and the last not-entirely-negative growth units) were compared for each species and for each soil. The probabi...

Most-probable-number (MPN) and membrane filtration (mF) techniques were evaluated with respect to selectivity, sensitivity, and efficiency in recovering Pseudomonas aeruginosa strains in hospital fluids and extramural water environments. Known numbers of cells of a naturally occurring strain of P. aeruginosa maintained in distilled water or cells subcultured on Standard Methods agar were added ...

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In Bayesian networks, a Most Probable Explanation (MPE) is a complete variable instantiation with the highest probability given the current evidence. In this paper, we discuss the problem of finding robustness conditions of the MPE under single parameter changes. Specifically, we ask the question: How much change in a single network parameter can we afford to apply while keeping the MPE unchang...

Probabilistic databases (PDBs) have been widely studied in the literature, as they form the foundations of large-scale probabilistic knowledge bases like NELL and Google’s Knowledge Vault. In particular, probabilistic query evaluation has been investigated intensively as a central inference mechanism. However, despite its power, query evaluation alone cannot extract all the relevant information...

It has recently been suggested, by Firouzjahi, Sarangi, and Tye, that string-motivated modifications of the Hartle-Hawking wave function predict that our Universe came into existence from “nothing” with a de Sitter-like spacetime geometry and a spacetime curvature similar to that of “low-scale” models of Inflation. This means, however, that the Universe was quite large at birth. It would be pre...

This doctoral dissertation introduces an algorithm for constructing the most probable Bayesian network from data for small domains. The algorithm is used to show that a popular goodness criterion for the Bayesian networks has a severe sensitivity problem. The dissertation then proposes an information theoretic criterion that avoids the problem. Computing Reviews (1998)