1 We present a conditional density model of river runoff given covariate information 2 which includes precipation at four surrounding stations. The proposed model is non3 parametric in the central part of the distribution and relies on Extreme-Value Theory 4 parametric assumptions for the upper tail of the distribution. From the trained con5 ditional density model, we can compute quantiles of v...

A natural Bayesian approach for mixture models with an unknown number of components is to take the usual finite mixture model with symmetric Dirichlet weights, and put a prior on the number of components—that is, to use a mixture of finite mixtures (MFM). The most commonly-used method of inference for MFMs is reversible jump Markov chain Monte Carlo, but it can be nontrivial to design good reve...

We present a framework for the identification of cell subpopulations in flow cytometry data based on merging mixture components using the flowClust methodology. We show that the cluster merging algorithm under our framework improves model fit and provides a better estimate of the number of distinct cell subpopulations than either Gaussian mixture models or flowClust, especially for complicated ...

In this paper the steady state performance of a variable conductance heat pipe, which its working fluid consists of two components (R11+ R113) is modeled. The role of the liquid film momentum and axial normal stresses are considered by use of the mean property of mixture and the governing equations are solved by conventional numerical methods. The results of the present model have been compared...

Mixture models have received a great deal of attention in statistics due to the wide range applications found recent years. This paper discusses finite mixture model Birnbaum–Saunders distributions with G components, which is an important supplement that developed by Balakrishnan et al. (J Stat Plann Infer 141:2175–2190, 2011) who considered two components. Our proposal enables modeling proper ...

Motivated by generating personalized recommendations using ordinal (or preference) data, we study the question of learning a mixture of MultiNomial Logit (MNL) model, a parameterized class of distributions over permutations, from partial ordinal or preference data (e.g. pair-wise comparisons). Despite its long standing importance across disciplines including social choice, operations research a...

The series on average hours worked in the manufacturing sector is a key leading indicator of the U.S. business cycle. The paper deals with robust estimation of the cyclical component for the seasonally adjusted time series. This is achieved by an unobserved components model featuring an irregular component that is represented by a Gaussian mixture with two components. The mixture aims at captur...

A phonetic tied-mixture (PTM) model for efficient large vocabulary continuous speech recognition is presented. It is synthesized from context-independent phone models with 64 mixture components per state by assigning different mixture weights according to the shared states of triphones. Mixtures are then re-estimated for optimization. The model achieves a word error rate of 7.0% at 20k-word dic...

We show that one-dimensional binary mixtures of bosons or of a boson and a spin-polarized fermion are Luttinger liquids with the following instabilities: (i) For different particle densities, strong attraction between the mixture components leads to collapse, while strong repulsion leads to demixing, and (ii) For a low-density mixture of two gases of impenetrable bosons (or a spin-polarized fer...