There are many different distributions that may fit into a specific simulation task. Though exponential, normal and Poisson distributions are the ones used most often, others such as gamma and Weibull distributions are useful and important as well.

Here is a list of commonly used distributions.

**Binomial**- Models the number of successes in
*n*trials, when the trials are independent with common success probability,*p*. **Negative Binomial including the geometric distribution**- Models the
number of trials required to achieve
*k*successes. **Poisson**- Models the number of independent events that occur in a
fixed amount of time or space.
**Normal**- Models the distribution of a process that can be thought
of as the sum of a number of component processes.
**Log-normal**- Models the distribution of a process that can be
thought of as the product of a number of component processes.
**Exponential**- Models the time between independent events, or
a process time which is memoryless.
**Gamma**- An extremely flexible distribution used to model non-negative
random variables.
**Beta**- An extremely flexible distribution used to model bounded
random variables.
**Erlang**- Models processes that can be viewed as the sum of several
exponentially distributed processes.
**Weibull**- Models the time-to-failure for components.
**Discrete or Continuous Uniform**- Models complete uncertainty, since
all outcomes are equally likely.
**Triangular**- Models a process when only the minimum, most-likely,
and maximum values of the distribution are known.
**Empirical**- Resamples from the actual data collected.