sympy.stats.Wald() in Python

sympy.stats.Wald() in Python

The Wald function in sympy.stats represents a Wald (or inverse Gaussian) continuous random variable. The Wald distribution is characterized by two parameters, mean (��) and scale (��). It is mainly used in survival analysis and reliability studies.

Here's a basic overview and demonstration of the Wald distribution in sympy.stats:

  • Creating a Wald Random Variable:

You can create a Wald-distributed random variable using the Wald function by specifying its parameters.

from sympy.stats import Wald, density from sympy import Symbol mu = Symbol("mu", positive=True) # mean lambd = Symbol("lambda", positive=True) # scale z = Symbol("z") # Define a Wald random variable with a specific mean and scale W = Wald("W", mu, lambd)
  • Getting the Density Function:

You can obtain the probability density function (PDF) of the Wald random variable using the density function.

wald_density = density(W)(z) print(wald_density)
  • Computing Moments and Other Properties:

With the random variable defined, you can compute various properties like expected value, variance, etc.:

from sympy.stats import E, variance expectation = E(W) var = variance(W) print(f"Expectation: {expectation}") print(f"Variance: {var}")
  • Substituting Parameters for Specific Values:

If you want to evaluate the density or any other property at specific parameter values, you can use the subs method.

density_at_values = wald_density.subs({mu: 1, lambd: 2, z: 0.5}) print(density_at_values)

Remember, the Wald distribution and its properties can be explored in-depth mathematically, and SymPy provides a convenient way to handle and manipulate these mathematical properties in symbolic form.

More Tags

plotly-python fullcalendar-4 coldfusion ontouchlistener sudo c#-ziparchive android-virtual-device ms-access react-redux

More Programming Guides

Other Guides

More Programming Examples