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information matrix sentence in Hindi

"information matrix" meaning in Hindiinformation matrix in a sentence
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  • In statistics and information theory, the "'expected formation matrix "'of a likelihood function L ( \ theta ) is the matrix inverse of the Fisher information matrix of L ( \ theta ), while the "'observed formation matrix "'of L ( \ theta ) is the inverse of the observed information matrix of L ( \ theta ).
  • In statistics and information theory, the "'expected formation matrix "'of a likelihood function L ( \ theta ) is the matrix inverse of the Fisher information matrix of L ( \ theta ), while the "'observed formation matrix "'of L ( \ theta ) is the inverse of the observed information matrix of L ( \ theta ).
  • The lower two diagonal entries of the Fisher information matrix, with respect to the parameter " a " ( the minimum of the distribution's range ) : \ mathcal { I } _ { a, a }, and with respect to the parameter " c " ( the maximum of the distribution's range ) : \ mathcal { I } _ { c, c } are only defined for exponents ? > 2 and ? > 2 respectively.
  • The expressions in terms of the log geometric variances and log geometric covariance occur as functions of the two parameter " X " ~ Beta ( ?, ? ) parametrization because when taking the partial derivatives with respect to the exponents ( ?, ? ) in the four parameter case, one obtains the identical expressions as for the two parameter case : these terms of the four parameter Fisher information matrix are independent of the minimum " a " and maximum " c " of the distribution's range.
  • Jaynes states : " " interpret the Bayes-Laplace ( Beta ( 1, 1 ) ) prior as describing not a state of complete ignorance ", but the state of knowledge in which we have observed one success and one failure . . . once we have seen at least one success and one failure, then we know that the experiment is a true binary one, in the sense of physical possibility . " Jaynes does not specifically discuss Jeffreys prior Beta ( 1 / 2, 1 / 2 ) ( Jaynes discussion of " Jeffreys prior " on pp . 181, 423 and on chapter 12 of Jaynes book refers instead to the improper, un-normalized, prior " 1 / p " introduced by Jeffreys in the 1939 edition of his book, seven years before he introduced what is now known as Jeffreys'invariant prior : the square root of the determinant of Fisher's information matrix . " " 1 / p " is Jeffreys'( 1946 ) invariant prior for the exponential distribution, not for the Bernoulli or binomial distributions " ).
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