- Referenced in 364 articles
- Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods. The project...
- Referenced in 416 articles
- systems. PRISM supports three probabilistic models: discrete-time Markov chains, Markov decision processes and continuous ... time Markov chains. Analysis is performed through model checking such systems against specifications written...
- Referenced in 209 articles
- program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation ... plaftorm for experimentation with ideas in Bayesian modelling. JAGS is licensed under the GNU General...
- Referenced in 330 articles
- hierarchical models. Unfortunately, fitting such models involves computationally intensive Markov chain Monte Carlo (MCMC) methods ... wide variety of Gaussian spatial process models for univariate as well as multivariate point-referenced...
- Referenced in 101 articles
- such as support vector machines, hidden Markov models, multiple kernel learning, linear discriminant analysis...
- Referenced in 286 articles
- image analysis are familiar with Gaussian Markov Random Fields (GMRFs), and they are traditionally among ... longitudinal and survival data, spatio-temporal models, graphical models, and semi-parametric statistics. With ... comprehensive reference on the subject.par Gaussian Markov Random Fields: Theory and Applications provides such ... complex hierarchical models, in which statistical inference is only possible using Markov Chain Monte Carlo...
- Referenced in 70 articles
- probabilistic model checker MRMC. The Markov Reward Model Checker (MRMC) is a software tool ... probabilistic models. It supports PCTL and CSL model checking, and their reward extensions. Distinguishing features ... analysis for continuous-time Markov decision processes (CTMDPs) and CSL model checking by discrete-event...
- Referenced in 48 articles
- Multi-state Markov and hidden Markov models in continuous time. Functions for fitting general continuous ... time Markov and hidden Markov multi-state models to longitudinal data. A variety of observation ... continuously-observed processes, and censored states. Both Markov transition ... rates and the hidden Markov output process can be modelled in terms of covariates, which...
- Referenced in 34 articles
- popular probabilistic modeling formalisms, the hidden Markov model and Bayesian networks, are described by PRISM...
- Referenced in 33 articles
- generation techniques, as well as symbolic CTL model-checking algorithms, are available. For the study ... available when the underlying process is a Markov chain. In addition, discrete-event simulation ... process, but certain classes of non-Markov models can still be solved numerically. Finally, since...
- Referenced in 33 articles
- sequence alignments, consensus secondary structures and covariance models (CMs). The families in Rfam break down ... complicated relative of the profile hidden Markov models (HMMs) used by Pfam. CMs can simultaneously...
- Referenced in 31 articles
- chain analyzer, a software package for Markov modeling MARCA is a software package designed...
- Referenced in 21 articles
- natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications ... extraction from text. Algorithms include Hidden Markov Models, Maximum Entropy Markov Models, and Conditional Random...
- Referenced in 55 articles
- range of phylogenetic and evolutionary models. MrBayes uses Markov chain Monte Carlo (MCMC) methods...
- Referenced in 42 articles
- types for performance, reliability and performability modeling. Model types include combinatorial one such as fault ... ones such as Markov and semi-Markov reward models as well stochastic Petri nets. Steady...
- Referenced in 139 articles
- analysis of discrete-time and continuous-time Markov chains up to 100 states. The other ... waiting-time probabilities for basic queueing models (M/G/1 queue, M/M/c queue, M/D/c queue, G/M/c queue...
- Referenced in 29 articles
- regulatory sequence motifs. BioProspector uses Markov background to model the base dependencies of non-motif ... reported motifs. The parameters of the Markov background model are either estimated from user-specified...
- Referenced in 104 articles
- fitting multilevel models. It uses both maximum likelihood estimation and Markov Chain Monte Carlo (MCMC ... other additional features). MLwiN represents multilevel models using mathematical notation including Greek letters...
- Referenced in 17 articles
- package ggm: A package for Graphical Markov Models. Functions for analyzing and fitting Graphical Markov...
- Referenced in 24 articles
- loads; Theoretical density of rainflow cycles Sea modelling: Simulation of linear and non-linear Gaussian ... value analysis; Kernel density estimation, Hidden markov models...