discrete_rv¶

Filename: discrete_rv.py

Authors: Thomas Sargent, John Stachurski

Generates an array of draws from a discrete random variable with a specified vector of probabilities.

class quantecon.discrete_rv.DiscreteRV(q)[source]¶

Bases: object

Generates an array of draws from a discrete random variable with vector of probabilities given by q.

Parameters:

q : array_like(float)

Nonnegative numbers that sum to 1

Attributes

`q`	Getter method for q.

Q	(array_like(float)) The cumulative sum of q

Methods

draw([k]) Returns k draws from q.

draw(k=1)[source]¶

Returns k draws from q.

For each such draw, the value i is returned with probability q[i].

Parameters:

k : scalar(int), optional

Number of draws to be returned

Returns:

array_like(int)

An array of k independent draws from q

q¶: Getter method for q.

quantecon.discrete_rv.uniform(low=0.0, high=1.0, size=None)¶

Draw samples from a uniform distribution.

Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high). In other words, any value within the given interval is equally likely to be drawn by uniform.

Parameters:

low : float, optional

Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0.

high : float

Upper boundary of the output interval. All values generated will be less than high. The default value is 1.0.

size : int or tuple of ints, optional

Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned.

Returns:

out : ndarray

Drawn samples, with shape size.

See also

randint: Discrete uniform distribution, yielding integers.
random_integers: Discrete uniform distribution over the closed interval [low, high].
random_sample: Floats uniformly distributed over [0, 1).
random: Alias for random_sample.
rand: Convenience function that accepts dimensions as input, e.g., rand(2,2) would generate a 2-by-2 array of floats, uniformly distributed over [0, 1).

Notes

The probability density function of the uniform distribution is

\[p(x) = \frac{1}{b - a}\]

anywhere within the interval [a, b), and zero elsewhere.

Examples

Draw samples from the distribution:

>>> s = np.random.uniform(-1,0,1000)

All values are within the given interval:

>>> np.all(s >= -1)
True
>>> np.all(s < 0)
True

Display the histogram of the samples, along with the probability density function:

>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s, 15, normed=True)
>>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')
>>> plt.show()