Markov Chains and Dynamic Markov Chains
A markov_chain is a graph in which each edge has an associated
nonnegative integer weight. For every node with at least one outgoing
edge the total weight of the outgoing edges must be positive.
A random walk in a markov chain starts at some node s and then
performs steps according to the following rule:
 Initially,
s is the current node
 In the general step, suppose that node
v is the current
node.
 If
v has no outgoing edge no further step can be
taken.
 If
e _{0}, ..., e _{d  1}
are the edges out of v , the walk follows edge e _{i}
with probability proportional to w [e _{i}]
for all i, 0 <= i < d .
The target node of the chosen edge becomes the new current node.
Example for Markov
Chains
In a dynamic_markov_chain edge weights can be changed after creation.
Tips
Markov Chains and Dynamic Markov Chains are very special
data types. Use them if the data type fits your needs. 
See also:
Graphs and Related Data Types
Graph
Algorithms
Manual Entries:
Manual Page Markov Chains
Manual Page Dynamic Markov Chains
