STATISTICAL MODELS PRODUCED FROM FISHER INFORMATION FUNCTION | ||
| Baghdad College of Economic sciences University | ||
| Article 1, Volume 0, Issue 42, August 2018, Pages 377-388 | ||
| Authors | ||
| Azzam A.Tawfiq; ; Prof. Dr. Ahmed Al; Dr. Shakir Naji Mahmood; Assistant Lecturer Ali yahya Gheni; Dr. Bensaid Mohamed; Leila Zekraoui; Naji M. Sahib; Sana Ahmed Kadhom Sana Ahmed Kadhom | ||
| Abstract | ||
| Statistics the science of extracting information from data appears the most natural field of application of information theoretic methods in statistics. the Fisher information I ( θ) is the variance of score . it is named in honor of its inventor the statistician R. A .Fisher .the fisher information is the amount of information that an observable random variable X carries about unobservable parameter θ upon which the likelihood function of X,L(θ)=F(X;θ),depends. The likelihood function is the joint probability of the data , the Xs , conditional on the value of θ, as a function of θ. Since the expectation of the score is zero , the variance is simply the second moment of the likelihood function with respect to θ. Hence the Fisher information can be written I(θ) = E { [ϑ/ϑθ ln f ( x ,θ)]2|θ } Which implies 0 ≤ I (θ) Keywords maximum likelihood --- Leibnitz rule --- fisher information matrix. | ||
| Keywords | ||
| maximum likelihood; Leibnitz rule; fisher information matrix; infrastructure as a Service; Platform as a Service; Software as a Service; service oriented architecture; Access control; China; EU exchange goods and services; Fourth order eigenvalue problem; operator; boundary condition; Eigenvalue; Eigenfunction; completeness; minimality and basis; Data Hiding; Steganography; Binary images; security; Similarity | ||
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