In this work, we study the asymptotic distribution of the normalized sum of independent, identically distributed random variables under the finite mixture models. In the Theorem we give necessary conditions for a distribution function of a mixed population with k components to belong to the domain of attraction of an α-stable distribution, by assuming that each component of the mixture also per...

Voice transformation is the process of using a small amount of speech data from a target speaker to build a transformation model that can be used to generate arbitrary speech that sounds like the target speaker. One common current technique is building Gausian Mixture Models to map spectral aspects from source to target speakers. This paper proposes the use of duration models to improve the tra...

In our paper we recall some known mixture operators and their properties. We introduce sufficient conditions for weighting functions to ensure the monotonicity of the corresponding operators. We introduce new applications of mixture operators in the insurance, namely in the additional insurance.

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...