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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 values assigned to factors for the individual sample units in a factor analysis. The most common approach is "regression method". When the factors are seen as the random variab
data modelling
Independent component analysis (ICA) is the technique for analyzing the complex measured quantities thought to be mixtures of other more fundamental quantities, into their fundamen
Window estimates is a term which occurs in the context of the both frequency domain and time domain estimation for the time series. In the previous it generally applies to weights
replacement problem
K-means cluster analysis is the method of cluster analysis in which from an initial partition of observations into K clusters, each observation in turn is analysed and reassigned,
moving and semi average method graphical reprsentation
Law of likelihood : Within framework of the statistical model, a particular set of data supports one statistical hypothesis or assumption better than another if the likelihood of t
The biggest and smallest variate values among the sample of observations. Significant in various regions, for instance flood levels of the river, speed of wind and snowfall.
How large would the sample need to be if we are to pick a 95% confidence level sample: (i) From a population of 70; (ii) From a population of 450; (iii) From a population of 1000;
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