Truncated discrete exponential distribution

The distribution trunc_discrete_exp is equivalent to SciPy’s boltzmann distribution.

The parameters are lam > 0 (real parameter) and n > 0 (integer parameter).

The support is {0, 1, 2, …, n - 1}.

The probability mass function is p(k) = C*exp(-lam*k), where C is a normalization constant.

The distribution is uniform if lam == 0.

mpsci.distributions.trunc_discrete_exp.cdf(k, lam, n)

Cumulative distribution function of the truncated discrete exponential distribution.

mpsci.distributions.trunc_discrete_exp.logpmf(k, lam, n)

Logarithm of PMF of the truncated discrete exponential distribution.

mpsci.distributions.trunc_discrete_exp.mean(lam, n)

Mean of the truncated discrete exponential distribution.

mpsci.distributions.trunc_discrete_exp.pmf(k, lam, n)

Probability mass function of the truncated discrete exponential distribution.

mpsci.distributions.trunc_discrete_exp.sf(k, lam, n)

Survival function of the truncated discrete exponential distribution.

mpsci.distributions.trunc_discrete_exp.support(lam, n)

Support of the truncated discrete exponential distribution.

The support is the integers 0, 1, 2, …, n - 1; this is implemented by returning range(n). That is, the return value is the range instance, not a sequence.

Examples

>>> from mpsci.distributions import trunc_discrete_exp
>>> sup = trunc_discrete_exp.support(2.5, 6)
>>> [k for k in sup]
[0, 1, 2, 3, 4, 5]

mpsci.distributions.trunc_discrete_exp.var(lam, n)

Variance of the truncated discrete exponential distribution.