Marmote Core
The project aims at realizing the prototype of a software environment dedicated to modeling with Markov chains.
Class Hierarchy
This inheritance list is sorted roughly, but not completely, alphabetically:
[detail level 123]
 CalgorithmicSamplerAbstraction for algorithms that produce samples of some unspecified distribution
 CpsiSamplerAbstraction for algorithms that produce samples of some unspecified distribution
 CAliasAliasing data
 CCostSimulation data for CFTP algorithm
 CDistributionA class for representing probability distributions
 CdiscreteDistributionThe general discrete distribution with finite support
 CbernoulliDistributionThe Bernoulli distribution with two values
 CdiracDistributionThe Dirac distribution concentrated at some point
 CuniformDiscreteDistributionThe uniform discrete distribution
 CexponentialDistributionThe class representing the (negative) exponential distribution
 CgeometricDistributionThe geometric distribution with starting value 0. The parameter "p" is called "ratio". The Geometric distribution is discrete but does not inherit from discreteDistribution because its range is infinite
 CpoissonDistributionThe Poisson distribution. The parameter is called "lambda". The Poisson distribution is discrete but does not inherit from discreteDistribution because its range is infinite
 CtemplateDistributionThe general template distribution to be instantiated
 CuniformDistributionThe continuous uniform distribution over some interval
 Celement
 Cextension
 CHBFStorage of matrix in Harwell Boeing Format
 Citem
 CLAW_DESC
 CLAW_LIST
 CmarkovChainMarkov Chain class
 Cfelsenstein81Ajout de fonctionalites sur les matrices F81 (Felsenstein 81) Ce sont des matrices 4x4 qui sont caracterisees par: un vecteur de 4 probabilites p[0], p[1], p[2], p[3] un parametre de vitesse mu > 0
 Chomogeneous1DBirthDeathThe 1-dimensional birth and death process with homogeneous transition rates. This model is characterized by:
 Chomogeneous1DRandomWalkThe 1-dimensional random walk with homogeneous transition probabilities. This model is characterized by:
 ChomogeneousMultiDRandomWalkThe general d-dimensional random walk with homogeneous transition probabilities. This model is characterized by:
 CmarmoteSetThe mother class representing abstract sets
 CmarmoteBox
 CmarmoteIntervalThe class describing a finite integer interval
 CsimulationResultThe class for transmitting (Monte Carlo) simulation results between objects. Simulation results may be diverse: this structure should be able to accomodate each of the results, even if they are not all present at the same time. Results include: trajectories, empirical frequencies
 CtransitionStructureAbstract class for transition structures. These are structures which describe transitions to one state to another one, to which is attached a numeric label. Typical instances should be one-step transition matrices of discrete-time Markov chains, and infinitesimal generators of continuous-time Markov chains. It is also possible that the origin state space and the destination state space are different
 CeventMixture
 CmultiDimHomTransitionClass for multidimensional, homogeneous random walk transition structures. These are characterized by
 CsparseMatrixClass sparseMatrix: implementation of a transition structure using the sparse matrix data structure. Elements of the transition structure (matrix) are stored by row, using two arrays containing indices of columns, and values of entries. A priori, only non-zero entries are stored but this is not a requirement