Results 331 to 340 of about 5,309,310 (378)

A Note with Quantiles of the Asymptotic Distribution of the Maximum Likelihood Cointegration Rank Test Statistics

, 1992
The recent literature on maximum likelihood cointegration theory studies Gaussian vector autoregression (VAR) models allowing for some deterministic components in the form of polynomials in time.
Michael Osterwald-Lenum
semanticscholar   +1 more source

The Method of Maximum Likelihood

1999
In the last chapter we introduced the concept of parameter estimation. We have also described the desirable properties of estimators, though without specifying how such estimators can be constructed in a particular case. We have derived estimators only for the important quantities expectation value and variance. We now take on the general problem.
openaire   +2 more sources

Maximum likelihood autocalibration

Image and Vision Computing, 2011
This paper addresses the problem of autocalibration, which is a critical step in existing uncalibrated structure from motion algorithms that utilize an initialization to avoid the local minima in metric bundle adjustment. Currently, all known direct (not non-linear) solutions to the uncalibrated structure from motion problem solve for a projective ...
Stuart B. Heinrich, Wesley E. Snyder, Jan-Michael Frahm   +2 more
openaire   +2 more sources

Mixture densities, maximum likelihood, and the EM algorithm

, 1984
The problem of estimating the parameters which determine a mixture density has been the subject of a large, diverse body of literature spanning nearly ninety years.
R. Redner, H. Walker
semanticscholar   +1 more source

[36] Maximum likelihood methods

1990
Publisher Summary This chapter examines the maximum likelihood (ML) method and its general principle for nucleotide sequence data. Each nucleotide site is considered separately in Felsenstein's method. When each site is assumed to evolve at the same evolutionary rate, however, a more essential unit of comparison for the ML method is the “nucleotide ...
openaire   +3 more sources

The Maximum Likelihood Principle

1996
In this chapter we explore the various uses of the maximum likelihood principle in discrimination. In general, the principle is only applicable if we have some a priori knowledge of the problem at hand. We offer definitions, consistency results, and examples that highlight the advantages and shortcomings.
László Györfi, Gábor Lugosi, Luc Devroye   +2 more
openaire   +2 more sources

Information Theory and an Extension of the Maximum Likelihood Principle

, 1973
H. Akaike
semanticscholar   +1 more source

Evolutionary trees from DNA sequences: A maximum likelihood approach

Journal of Molecular Evolution, 2005
J. Felsenstein
semanticscholar   +1 more source

Maximum likelihood extended gradient‐based estimation algorithms for the input nonlinear controlled autoregressive moving average system with variable‐gain nonlinearity

International Journal of Robust and Nonlinear Control, 2021
Yamin Fan, Ximei Liu
exaly  

Maximum Likelihood Estimation

1982
This chapter deals with maximum likelihood estimation based on n independent observations X1,...,Xn from the distribution N ⊣ (λ, χ, Ψ).
openaire   +2 more sources