نتایج جستجو برای: population parameters

تعداد نتایج: 1241088  

2000
T. CHRISTIANSEN

The fixed energy scattering matrix is defined on a perturbed stratified medium, and for a class of perturbations, its main part is shown to be a Fourier integral operator on the sphere at infinity. This is facilitated by developing a refined limiting absorption principle. The symbol of the scattering matrix is shown to determine the asymptotics of a large class of perturbations.

2000
E. A. Kolganova

In this work we compare two different approaches to calculation of the three-body resonances on the basis of Faddeev differential equations. The first one is the complex scaling approach. The second method is based on an immediate calculation of resonances as zeros of the three-body scattering matrix continued to the physical sheet.

Journal: :Caspian journal of internal medicine 2013
Shahran Ala Fatemeh Zanad Mohammad Reza Shiran

BACKGROUND Omeprazole is metabolized predominantly by CYP2C19, a polymorphically expressed enzymes that show marked interindividual and interethnic variation. These variations cause a substantial differences that have been reported in the pharmacokinetics of omeprazole. The aim of the present study was to evaluate the pharmacokinetic parameters of omeprazole in a random Iranian population. ME...

2013
E. Korotyaev ALEXEI IANTCHENKO EVGENY KOROTYAEV

We consider the 1D massless Dirac operator on the real line with compactly supported potentials. We study resonances as the poles of scattering matrix or equivalently as the zeros of modified Fredholm determinant. We obtain the following properties of the resonances: 1) asymptotics of counting function, 2) estimates on the resonances and the forbidden domain, 3) the trace formula in terms of re...

2014
RICHARD M. MCDERMID MICHELE CAPPELLARI KATHERINE ALATALO MAXIME BOIS MARTIN BUREAU ALISON F. CROCKER ROGER L. DAVIES TIMOTHY A. DAVIS ERIC EMSELLEM SADEGH KHOCHFAR DAVOR KRAJNOVIĆ RAFFAELLA MORGANTI PAOLO SERRA ANNE-MARIE WEIJMANS LISA M. YOUNG

We report on empirical trends between the dynamically determined stellar initial mass function (IMF) and stellar population properties for a complete, volume-limited sample of 260 early-type galaxies from the ATLAS project. We study trends between our dynamically-derived IMF normalisation αdyn ≡ (M/L)stars/(M/L)Salp and absorption line strengths, and interpret these via single stellar populatio...

Journal: :Genetics 2006
Rong Jiang Paul Marjoram Justin O Borevitz Simon Tavaré

This article is concerned with a statistical modeling procedure to call single-feature polymorphisms from microarray experiments. We use this new type of polymorphism data to estimate the mutation and recombination parameters in a population. The mutation parameter can be estimated via the number of single-feature polymorphisms called in the sample. For the recombination parameter, a two-featur...

Journal: :Bioinformatics 2006
Mary K. Kuhner

UNLABELLED We present a Markov chain Monte Carlo coalescent genealogy sampler, LAMARC 2.0, which estimates population genetic parameters from genetic data. LAMARC can co-estimate subpopulation Theta = 4N(e)mu, immigration rates, subpopulation exponential growth rates and overall recombination rate, or a user-specified subset of these parameters. It can perform either maximum-likelihood or Bayes...

2003
Giorgio E. Montanari M. Giovanna Ranalli

Calibration is commonly used in survey sampling to include auxiliary information at the estimation stage. Calibrating the observation weights on the population means (or totals) of the auxiliary variables implicitly assumes on a linear superpopulation regression model. When auxiliary information is available for all units in the population, more complex modeling can be handled by means of model...

Journal: :International journal of epidemiology 2004
B Rachet

WinBUGS code to fit the multivariate Bayesian relative risk model: model{ for( i in 1 : N ) { for( j in 1 : K ) { Y[i, j] ~ dpois(lambda[i, j]) # distribution of observations lambda[i, j] E[i, j] * theta[i, j] theta[i, j] exp(phi[ i, j]) # log parametrization } phi[i, 1:K ] ~ dmnorm(mu[ ], Omega[, ]) } for(j in 1:K){ mu[j] ~ dunif( 2,2) } Omega[1:K, 1:K ] ~ dwish(R[, ], 12) # Wishart on prec. m...

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