Results 321 to 330 of about 2,034,768 (362)
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1974
In den vorausgegangenen Kapiteln wurden Parameterschatzmethoden behandelt, bei denen keine besonderen Annahmen uber die Verteilungsdichte des Storsignals oder Fehlersignals gemacht werden musten. Die Annahme von Modellen, deren Fehlersignal linear in den Parametern ist, erlaubte dann bei der nichtrekursiven Methode der kleinsten Quadrate eine direkte ...
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In den vorausgegangenen Kapiteln wurden Parameterschatzmethoden behandelt, bei denen keine besonderen Annahmen uber die Verteilungsdichte des Storsignals oder Fehlersignals gemacht werden musten. Die Annahme von Modellen, deren Fehlersignal linear in den Parametern ist, erlaubte dann bei der nichtrekursiven Methode der kleinsten Quadrate eine direkte ...
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The Method of Maximum Likelihood
1999In 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.
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Maximum likelihood autocalibration
Image and Vision Computing, 2011This 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 +2 more
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[36] Maximum likelihood methods
1990Publisher 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 ...
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2019
This chapter recalls the basics of the estimation method consisting in maximizing the likelihood associated to the observations. The resulting estimators enjoy convenient theoretical properties, being optimal in a wide variety of situations. The maximum likelihood principle will be used throughout the next chapters to fit the supervised learning models.
Julien Trufin +2 more
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This chapter recalls the basics of the estimation method consisting in maximizing the likelihood associated to the observations. The resulting estimators enjoy convenient theoretical properties, being optimal in a wide variety of situations. The maximum likelihood principle will be used throughout the next chapters to fit the supervised learning models.
Julien Trufin +2 more
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The Maximum Likelihood Principle
1996In 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 +2 more
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1982
This chapter deals with maximum likelihood estimation based on n independent observations X1,...,Xn from the distribution N ⊣ (λ, χ, Ψ).
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This chapter deals with maximum likelihood estimation based on n independent observations X1,...,Xn from the distribution N ⊣ (λ, χ, Ψ).
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2006
Abstract This chapter discusses likelihood calculation for multiple sequences on a phylogenetic tree. As indicated at the end of Chapter 3, this is a natural extension to the parsimony method when we want to incorporate differences in branch lengths and in substitution rates between nucleotides. Likelihood calculation on a tree is also a
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Abstract This chapter discusses likelihood calculation for multiple sequences on a phylogenetic tree. As indicated at the end of Chapter 3, this is a natural extension to the parsimony method when we want to incorporate differences in branch lengths and in substitution rates between nucleotides. Likelihood calculation on a tree is also a
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