###### Stochastic Processes

###### Computer Simulation

###### Models, Biological

###### Models, Statistical

###### Markov Chains

A stochastic process such that the conditional probability distribution for a state at any future instant, given the present state, is unaffected by any additional knowledge of the past history of the system.

###### Models, Genetic

###### Biological Evolution

###### Population Dynamics

###### Ecosystem

###### Models, Theoretical

###### Monte Carlo Method

In statistics, a technique for numerically approximating the solution of a mathematical problem by studying the distribution of some random variable, often generated by a computer. The name alludes to the randomness characteristic of the games of chance played at the gambling casinos in Monte Carlo. (From Random House Unabridged Dictionary, 2d ed, 1993)

###### Evolution, Molecular

###### Algorithms

###### Selection, Genetic

###### Mutation

###### Computer Graphics

###### User-Computer Interface

###### Software

###### Internet

###### Data Display

###### Computational Biology

A field of biology concerned with the development of techniques for the collection and manipulation of biological data, and the use of such data to make biological discoveries or predictions. This field encompasses all computational methods and theories for solving biological problems including manipulation of models and datasets.

###### Bayes Theorem

A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.