Directed Acyclic Graph HMM with TAN Structured Emissions
Infer the backward probabilities for all the nodes of the dagHMM
Inferring the parameters of a dag Hidden Markov Model via the Baum-Wel...
Implementation of the Baum Welch Algorithm as a special case of EM alg...
Calculate the order in which nodes in the dag should be traversed duri...
Calculating the probability of occurance of particular values of covar...
Infer the forward probabilities for all the nodes of the dagHMM
Calculate the order in which nodes in the dag should be traversed duri...
Generating the inital emission probability distribution of the covaria...
Initializing dagHMM with given parameters
Calculating the probability of transition from multiple nodes to given...
Hidden Markov models (HMMs) are a formal foundation for making probabilistic models of linear sequence. They provide a conceptual toolkit for building complex models just by drawing an intuitive picture. They are at the heart of a diverse range of programs, including genefinding, profile searches, multiple sequence alignment and regulatory site identification. HMMs are the Legos of computational sequence analysis. In graph theory, a tree is an undirected graph in which any two vertices are connected by exactly one path, or equivalently a connected acyclic undirected graph. Tree represents the nodes connected by edges. It is a non-linear data structure. A poly-tree is simply a directed acyclic graph whose underlying undirected graph is a tree. The model proposed in this package is the same as an HMM but where the states are linked via a polytree structure rather than a simple path.