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- Roy, Vivekananda; Hobert, James P.
- Journal of the Royal Statistical Society 2007 v.69 no.4 pp. 607-623
- normal distribution, etc ; Markov chain; algorithms; variance; Show all 4 Subjects
- ... Consider a probit regression problem in which Y₁, [ellipsis (horizontal)], Yn are independent Bernoulli random variables such that [graphic removed] where xi is a p-dimensional vector of known covariates that are associated with Yi, β is a p-dimensional vector of unknown regression coefficients and Φ(·) denotes the standard normal distribution function. We study Markov chain Monte Carlo algorithms ...
- Mardia, Kanti V.; Taylor, Charles C.; Subramaniam, Ganesh K.
- Biometrics 2007 v.63 no.2 pp. 505-512
- normal distribution, etc ; algorithms; bioinformatics; biometry; data collection; models; protein secondary structure; Show all 7 Subjects
- ... A fundamental problem in bioinformatics is to characterize the secondary structure of a protein, which has traditionally been carried out by examining a scatterplot (Ramachandran plot) of the conformational angles. We examine two natural bivariate von Mises distributions--referred to as Sine and Cosine models--which have five parameters and, for concentrated data, tend to a bivariate normal distri ...
- Hu, Jing; Gao, Jian-Bo; Cao, Yinhe; Bottinger, Erwin; Zhang, Weijia
- Nucleic acids research 2007 v.35 no.5 pp. e35
- normal distribution, etc ; DNA; algorithms; bacterial artificial chromosomes; chromosome aberrations; methodology; neoplasms; pathogenesis; Show all 8 Subjects
- ... Developing effective methods for analyzing array-CGH data to detect chromosomal aberrations is very important for the diagnosis of pathogenesis of cancer and other diseases. Current analysis methods, being largely based on smoothing and/or segmentation, are not quite capable of detecting both the aberration regions and the boundary break points very accurately. Furthermore, when evaluating the acc ...
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