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The nonparametric Bayesian inference approach to using the finite mixture distributions for modelling data suspected of the containing distinct groups of observations; this approach does not need the number of mixture components to be known in before. The basic idea is that the Dirichlet procedure induces a prior distribution over the partitions of the data which can then be combined with the prior distribution over parameters and chance. The distribution over partitions can be generated incrementally using Chinese restaurant procedure.
The functions of the data and the parameters of interest which can be brought in use to conduct inference about the parameters when full distribution of the observations is unknown
when there is tie in sequencing then what we do
The studies conducted in the pharmaceutical industry to calculate the degradation of the new drug product or an old drug formulated or packaged in the new manner. The main study ob
Locally weighted regression is the method of regression analysis in which the polynomials of degree one (linear) or two (quadratic) are used to approximate regression function in
Described by the leading proponent as 'the conscientious, explicit, and judicious uses of present best evidence in making the decisions about the care of individual patients, and
Multitrait multi method model (MTMM) is the form of confirmatory factor analysis model in which the different techniques of measurement are used to measure each of the latent vari
Indirect least squares: An estimation technique used in the fitting of structural equation models. Commonly least squares are first used to estimate reduced form parameters. Usi
Probability weighting is the procedure of attaching weights equal to inverse of the probability of being selected, to each respondent's record in the sample survey. These weights
Regression to the mean is the procedure first noted by Sir Francis Galton that 'each peculiarity in man is shared by his kinsmen, but on average to the less degree.' Hence the ten
The Expectation/Conditional Maximization Either algorithm which is the generalization of ECM algorithm attained by replacing some of the CM-steps of ECM which maximize the constrai
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