Minimization of logic networks under a generalized cost function /

Includes bibliographical references.

Generalized decomposition of incomplete finite automata /

Vita.

Generalized Illiac IV urban land use planning model /

Includes bibliographies (p. 22).

The generalized element method /

"UILU-ENG 78 1738."

Deposition and washout computations based on the generalized Gaussian plume model /

"September 30, 1963."

Project Neos; a generalized study of processes for the realization of esign configurations in materials. [Final] technical documentary report no. ML-TDR-64-311. MTD project no.7-867.

Mode of access: Internet.

Generalized laboratory study of tsunami run-up /

Mode of access: Internet.

Probable economic effect redesignation of certain articles from certain countries as eligible for duty-free treatment under the U.S. generalized system of preferences : report to the President on investigation no. 332-284

Mode of access: Internet.

Generalized organization of large data-bases : a set-theoretic approach to relations /

Mode of access: Internet.

Comparison of generalized estimates of probable maximum precipitation with greatest observed rainfalls /

Mar. 1980.

A generalized network formulation for aperture problems /

"AFCRL-TR-75-0589."

Phase 1 : developing generalized driving while intoxicated (DWI) school curricula /

Mode of access: Internet.

An introduction to the use of generalized coordinates in mechanics and physics,

Mode of access: Internet.

Bubble detection in Brazil’s stock market: application of the generalized superior augmented Dickey-Fuller test

Considering the importance of the proper detection of bubbles in financial markets for policymakers and market agents, we used two techniques described in Diba and Grossman (1988b) and in Phillips, Shi, and Yu (2015) to detect periods of exuberance in the recent history of the Brazillian stock market. First, a simple cointegration test is applied. Secondly, we conducted several augmented, right-tailed Dickey-Fuller tests on rolling windows of data to determine the point in which there’s a structural break and the series loses its stationarity.

A generalized Markov sampler

A recent development of the Markov chain Monte Carlo (MCMC) technique is the emergence of MCMC samplers that allow transitions between different models. Such samplers make possible a range of computational tasks involving models, including model selection, model evaluation, model averaging and hypothesis testing. An example of this type of sampler is the reversible jump MCMC sampler, which is a generalization of the Metropolis-Hastings algorithm. Here, we present a new MCMC sampler of this type. The new sampler is a generalization of the Gibbs sampler, but somewhat surprisingly, it also turns out to encompass as particular cases all of the well-known MCMC samplers, including those of Metropolis, Barker, and Hastings. Moreover, the new sampler generalizes the reversible jump MCMC. It therefore appears to be a very general framework for MCMC sampling. This paper describes the new sampler and illustrates its use in three applications in Computational Biology, specifically determination of consensus sequences, phylogenetic inference and delineation of isochores via multiple change-point analysis.