Results 221 to 230 of about 828,317 (269)

A genotype likelihood function for DNA mixtures

Forensic Science International: Genetics, 2022
The recent advent of genetic genealogy has brought about a renewed interest in genome-scale forensic analyses, of which kinship estimation is a critical component. Most genomic kinship estimators consider SNPs (single nucleotide polymorphisms), often leveraging the co-inheritance of shared alleles to inform their analyses.
Benjamin, Crysup, August E, Woerner
openaire   +2 more sources

A reconfigurable architecture for the Phylogenetic Likelihood Function

2009 International Conference on Field Programmable Logic and Applications, 2009
As FPGA devices become larger, more coarse-grain modules coupled with large scale reconfigurable fabric become available, thus enabling new classes of applications to run efficiently, as compared to a general-purpose computer. This paper presents an architecture that benefits from the large number of DSP modules in Xilinx technology to implement ...
Nikolaos Alachiotis 0001, Alexandros Stamatakis, Euripides Sotiriades, Apostolos Dollas   +3 more
openaire   +1 more source

Comparing the Likelihood Functions of Phylogenetic Trees

Annals of the Institute of Statistical Mathematics, 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bar-Hen, A., Kishino, H.
openaire   +1 more source

A projected likelihood function for semiparametric models

Biometrika, 1992
Summary: In a sequence of papers, the first author [Can J. Stat. 12, 265-282 (1984; Zbl 0574.62084)], \textit{J. E. Hutton} and \textit{P. I. Nelson} [Stochastic Processes Appl. 22, 245-257 (1986; Zbl 0616.62113)], and \textit{V. P. Godambe} and \textit{C. C. Heyde} [Int. Stat. Rev.
McLeish, D. L., Small, Christopher G.
openaire   +2 more sources

Assigning a Likelihood Function

2020
As scientists, we want to know how to parameterise our models, make comparisons with other models, and quantify model predictive uncertainty. For all these purposes, measurement data are needed, but how exactly should we use the data? The answer is always the same: in the likelihood function.
openaire   +1 more source

On the Empirical Likelihood Ratio for Smooth Functions of M‐functionals

Scandinavian Journal of Statistics, 1997
It is known that the empirical likelihood ratio can be used to construct confidence regions for smooth functions of the mean, Fréchet differentiable statistical functionals and for a class of M‐functionals. In this paper, we argue that this use can be extended to the class of functionals which are smooth functions of M‐functionals.
openaire   +3 more sources

The Plausibility and Likelihood Functions

2003
The notion of likelihood is an important concept in modern statistics. In particular, the likelihood ratio has been used by several authors [19, 37] to measure the strength of the evidence represented by observations in statistical problems. This idea works fine when the goal is to evaluate the strength of the available evidence for a simple hypothesis
openaire   +1 more source

A deviance function for the quasi-likelihood method

Biometrika, 1993
Summary: We introduce a deviance function that can be used in conjunction with the quasi-likelihood method. The need for such functions arises when the quasi-log likelihood function is not uniquely defined. The deviance is obtained by projecting a pair of centered likelihood ratios onto the direct sum of two Hilbert spaces spanned by the observations ...
openaire   +2 more sources

Estimating Functions and Approximate Conditional Likelihood

Biometrika, 1987
The approximate conditional likelihood method proposed by \textit{D. R. Cox} and \textit{N. Reid}, J. R. Stat. Soc., Ser. B 49, 1-39 (1987; Zbl 0616.62006) is applied to the estimation of a scalar parameter \(\theta\), in the presence of nuisance parameters.
openaire   +2 more sources

Soft likelihood functions in combining evidence

Information Fusion, 2017
Abstract We develop an approach for flexible computation of likelihood functions of probabilistic evidence in the context of forensic crime investigations. An ordered weighted average (OWA) aggregation approach allows a softening of the strong likelihood constraint of requiring all such evidence.
Ronald R. Yager, Paul Elmore, Frederick E. Petry   +2 more
openaire   +1 more source