Determining photometric redshifts to high accuracy is paramount measure distances in wide-field cosmological experiments. With only information at hand, photo-zs are prone systematic uncertainties the intervening extinction and unknown underlying spectral-energy distribution of different astrophysical sources. Here, we aim resolve these model degeneracies obtain a clear separation between intri...

Abstract Bayesian nonparametric density estimation is dominated by single-scale methods, typically exploiting mixture model specifications, exception made for Pólya trees prior and allied approaches. In this paper we focus on developing a novel family of multiscale stick-breaking models that inherits some the advantages both mixtures trees. Our proposal based specification an infinitely deep bi...

Decreasing weight prior distributions for mixture models play an important role in nonparametric Bayesian inference. Various random probability measures with decreasing weights have been previously explored and it has shown that they provide efficient alternative to the more traditional Dirichlet process model. This ordering of implicitly alleviates so-called label switching problem, as larger ...

Abstract: Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm.   In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact,...

  Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm.   In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, ...