• # AutoClass

• Referenced in 69 articles [sw26092]
• maxima in the parameter space. This posterior probability rating allows direct comparison of alternate density ... mathematical development for the basic mixture model, describe the approximations needed for computational tractability, give...
• # MixSim

• Referenced in 34 articles [sw07914]
• defined as a sum of two misclassification probabilities, measures the degree of interaction between components ... clustering complexity of datasets simulated from mixtures. These datasets can then be used for systematic ... performance investigation of clustering and finite mixture modeling algorithms. Among other capabilities of MixSim, there...
• # GMMBAYES

• Referenced in 1 article [sw14964]
• methods for learning Gaussian mixture models. Furthermore, class conditional probability densities constructed by Gaussian mixture ... theory (e.g., Bayesian inference) and Gaussian mixture model probability densities. The ML parameter estimation will...
• # Mixnet

• Referenced in 9 articles [sw22900]
• Mixture for Networks, a probabilistic model for random graphs. This model is a clustering model ... algorithms to estimate the model’s parameters as well as probabilities of class membership...
• # ContaminatedMixt

• Referenced in 11 articles [sw21014]
• when several models have to be fitted. Differently from the more popular mixtures of multivariate ... mild outliers via the maximum a posteriori probabilities procedure. To exemplify...
• # COMODE

• Referenced in 3 articles [sw36145]
• data under a simple multinomial mixture model. This model assumes that the positions ... independent multinomial distributions with four cell probabilities. We address supervising the search for DNA binding ... interactions. We extend the simple multinomial mixture model to a constrained multinomial mixture model...
• # CUB

• Referenced in 1 article [sw32295]
• mixture of a uniform and a shifted binomial. cub estimates a probability model ... ordinal outcome variable, where the probability to observe a specific ordinal value (a preference ... given commodity, for instance) is modeled as a mixture of a Uniform and a shifted ... shifted Binomial distribution is the law of probability governing individual feeling on the item...
• # ForestFit

• Referenced in 1 article [sw31035]
• Modelling with Applications in Forestry. Developed for the following tasks. I) Computing the probability density ... estimating the parameters of the eleven mixture models including mixture of Birnbaum-Saunders, BurrXII, Chen ... gamma, log-normal, and Weibull mixture models fitted to the grouped data through ... probability density function, cumulative distribution function, and generating realizations from gamma shape mixture model introduced...
• # MMG

• Referenced in 4 articles [sw29328]
• RESULTS: We introduce Mixture Model on Graphs (MMG), a novel probabilistic model to identify differentially ... efficient sampling strategy to infer posterior probabilities of differential ... expression, as well as posterior probabilities over the model parameters. We assess our method ... artificial data demonstrating significant improvements over standard mixture model clustering. Analysis of our model results...
• # pyMEF

• Referenced in 2 articles [sw07455]
• Gaussian mixture models are a widespread tool for modeling various and complex probability density functions ... promising stochastic modeling methods include Dirichlet process mixtures and $k$-maximum likelihood estimators. Most...
• # blasso

• Referenced in 13 articles [sw06769]
• resulting in an infinite mixture of elastic net regression models that allows for adaptive, data ... about model specification is addressed from a Bayesian perspective by assigning prior probabilities...
• # rankdist

• Referenced in 4 articles [sw29955]
• Based Ranking Models. Implements distance based probability models for ranking data. The supported distance metrics ... Weighted Kendall distance. Phi-component model and mixture models are also supported...
• # gmr

• Referenced in 1 article [sw38775]
• Gaussian mixture regression (GMR). GMR is a regression approach that models probability distributions rather than ... possible to model multimodal mappings. In GMR we rst learn a joint probability distribution ... procedure as in a standard Gaussian mixture model...
• # BAS

• Referenced in 5 articles [sw24118]
• mixtures of g-priors in GLMs of Li and Clyde (2015) . Other model selection criteria ... Empirical Bayes estimates of g. Sampling probabilities may be updated based on the sampled models...
• # gendist

• Referenced in 1 article [sw37215]
• gendist: Generated Probability Distribution Models. Computes the probability density function (pdf), cumulative distribution function ... values (rg) for the following general models : mixture models, composite models, folded models, skewed symmetric...
• # joda

• Referenced in 2 articles [sw20391]
• next proceeds in three steps: (1) Computing probabilities of differential expression of the genes ... package. The approach performs partially supervised mixture modeling of the perturbation data. (2) Computing regulation ... obtained as an average of the probabilities of differential expression over those perturbation experiments that...
• # BVSNLP

• Referenced in 5 articles [sw22350]
• tackle this problem, where it exploits mixture of point masses at zero and nonlocal priors ... variable selection such as Highest Posterior Probability Model (HPPM), Median Probability Model (MPM) and posterior...