The mixture Rasch model is a popular for analyzing multivariate binary data. drawback of this that the number estimated parameters substantially increases with an increasing latent classes, which, in turn, hinders interpretability parameters. This article proposes regularized estimation imposes some sparsity structure on class-specific item difficulties. We illustrate feasibility proposed model...

1 Variational Dirichlet Process mixtures Assume two infinite sets φ = {φ1, φ2, . . .} and v = {v1, v2, . . . , } of component parameter vectors and beta distributed random variables. Each φk is independently drawn from the prior pφ(φk|λ) with hyperparameter λ and each vk is independently drawn from B(vk|1, α) with hyperparameter α. Given these sets a Dirichlet Process (DP) mixture model is writ...

Figure 1: Bayesian Mixture Model For a Bayesian Mixture Model as shown in figure 1, as k →∞, we shall have G = ∑∞ c=1 πcδφc , where all the φc are i.i.d. samples from G0, while the random sequence {πc}c=1 sum up to one, shall be constructed by the “Stick Breaking” process [3]. Suppose there is a stick with length 1. Let βc ∼ Beta(1, α) for c = 1, 2, 3, . . . , and regard them as fractions we ta...

This paper examines the problem of simultaneously testing many independent multiple hypotheses within the minimum encoding framework. We introduce an efficient coding scheme for nominating the accepted hypotheses in addition to compressing the data given these hypotheses. This formulation reveals an interesting connection between multiple hypothesis testing and mixture modelling with the class ...