… design an object oriented framework for stochastic modeling … , jMarkov which models Markov
Chains, jQBD which models … models Phase Types Distributions and jMDP which models …

… MC modeling packages, highlight the flexibility of jMarkov. … focus of jMarkov is on the numerical
solution of MC models, … tool for modeling and analysis is to use stochastic simulation. In …

… outcomes, and offers several tools to validate these predictions … of joint model’s parameters
proceeds using Markov chain … First, we load the package and create 10 random splittings of …

… package MSGARCH, which implements Markov-switching GARCH (generalized autoregressive
conditional heteroscedasticity) models … The integer-valued stochastic variable st, defined …

… There are currently some packages in R that handle hidden Markov models but they lack a
number of features that we needed in our research. In particular, depmixS4 was designed to …

… science and engineering to reconcile Earth system models with data, including prediction
in … and deterministic/stochastic model output, and inference of the model parameters. Bayes …

… Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its …
an existing package primarily because it allows us to build and efficiently fit any model we like …

… package provides a set of interface functions to facilitate running Markov chain Monte Carlo
models … of the model, data and current status of the random number generators can be saved …

… approximating techniques based on stochastic simulation. Stochastic simulation, or Monte
Carlo, … devoted to perform Bayesian inference based on stochastic simulation, hence its sub- …

… stochastic simulation software package. StochKit2 provides highly efficient implementations
of several variants of Gillespie's stochastic simulation … as a Markov jump process that is …