Log-likelihood function
Estimation of Population Parameters and Recombination Rates From Single Nucleotide Polymorphisms
... the likelihood function as Pr(X | variability in the first two copies sampled) 5 Pr(variability in the first two copies sampled |X) Pr(X)/Pr(variability in the first two Figure ...2.—The ...
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Learning in Neural Spatial Interaction Models: A Statistical Perspective
... objective function is defined by the negative log-likelihood function and the search space by the parameter space of an origin constrained product unit neural spatial interaction ...maximum ...
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Interval Estimation for the Stress Strength Reliability with Bivariate Normal Variables
... signed log-likelihood ratio statistic defined in (10), and Q ( ) ψ is a statistic that is based on the log-likelihood function and an ancillary ...maximum likelihood estimate ...
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Modeling Expectations with Noncausal Autoregressions
... We discuss ML estimation and develop related tests for noncausal autoregressive models. Furthermore, based on a number of simulation experiments and our expe- rience with actual economic data, we propose the following ...
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The Convergence and Robustness of Cohort Extensions of Mortality Models
... the log-likelihood function and by adding a non-linearity constraint to the Cohort Effects as well as assuming linearity of the time effect Hunt and Villegas (2015) finds a solution to overcome some ...
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Asymptotics of fingerprinting and group testing: capacity-achieving log-likelihood decoders
... Note that for the uninformed setting, the simple and joint capacities are asymptotically equivalent, which allowed us to prove the result. For finite c, the joint capac- ity may be slightly higher than the simple ...
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Parameter redundancy and the existence of maximum likelihood estimates in log linear models
... a function of a smaller set of parameters, which are themselves functions of the initial ...their likelihood surface which precludes unique maximum likelihood estimates for some of the parameters ...
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Scaled Log Likelihood Ratios for the Detection of Abbreviations in Text Corpora
... The scaling methods described in section 3 have been applied to test corpora from English (Wall Street Journal, WSJ) and German (Neue Zürcher Zeitung, NZZ). The scaled log λ was calculated for all pairs of the ...
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Stochastic Composite Likelihood
... The nature of the Boltzmann chain constrains our feature set to only encode the particular token present at each position, or time index. In doing so we avoid having to model additional depen- dencies across time steps ...
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The Distribution of Returns
... The challenge is created by an absence of a unique or clear mechanism to model bankruptcy in the literature. Indeed, the plethora of ways to model bankruptcy generates a wide range of possible joint distributions. The ...
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A Class of k-Log-Convex Functions and their Applications to Some Special Functions
... 1-log-convex function (or 1-log-concave function, respectively) is equivalent to a positive and increasing (or decreasing, respectively) function and that a 2-log-convex ...
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Biological and economic management strategy evaluations of the eastern king prawn fishery
... Capricorn-Bunker Islands as juvenile nursery grounds, and migrating east-northeast to fishing grounds. Data for Moreton Bay (near Brisbane) are omitted because of the multispecies nature of landings from the Bay making ...
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Linear Maximum Likelihood Regression Analysis for Untransformed Log Normally Distributed Data
... a log-normal heteroscedastic distribution with certain pa- rameter ...approximately log-normal and thus exhibit a larger vari- ance than the one in a perfect log-normal ...
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Likelihood and Estimation
... the expected log likelihood and the problem of estimating the true value would.. become that of estimating that maximising value.[r] ...
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Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood
... During the past years different discriminative approaches have been proposed, which tend to decompose the problem into two tasks: (i) discriminative structure learning, and (ii) discriminative parameter learning. Greiner ...
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Unbiased Generative Semi-Supervised Learning
... unbiased likelihood estimate as we ...the log likelihood of all unlabelled elements, by a factor which is set using cross ...marginal likelihood of the labelled samples is not re-weighed this ...
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An Adaptive Framework for Acoustic Monitoring of Potential Hazards
... Robust recognition of general audio events constitutes a topic of intensive research in the signal processing community. This work presents an efficient methodology for acoustic surveillance of atypical situations which ...
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Ranking Relevant Verb Phrases Extracted from Historical Text
... which log likelihood setting is the best, since it depends on what evaluation metric we ...the log likelihood for the verb and the complement separately, and return the sum of these values ...
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Text 1 ABSTRAK pdf
... Apabila dalam proses estimasi parameter didapat persamaan akhir yang non linear maka tidak mudah memperoleh estimasi parameter tersebut, sehingga diperlukan suatu metode numerik untuk memecahkan persamaan non linear ...
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Obtaining Expected Maximum Log Likelihood Estimators
... equating the derivative of the sample likelihood to zero and solving for 0 is a plausible way of estimating 00.. Of course, it also maximises the sample.[r] ...
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