Choi-Williams distribution

Choi-Williams distribution :

The Choi-Williams distribution is a probability distribution that is used to model the time it takes for a Poisson process to first exceed a certain threshold. In other words, it describes the time it takes for a process that is characterized by random events occurring at a constant rate to reach a certain level of intensity.
One example of a situation where the Choi-Williams distribution could be used is in the analysis of a manufacturing process. Suppose that a factory produces a certain type of widget, and the rate at which these widgets are produced is constant. If we are interested in the time it takes for the factory to produce a certain number of widgets, we can use the Choi-Williams distribution to model this process.
To understand how the Choi-Williams distribution works, it is helpful to first understand the Poisson process. A Poisson process is a type of stochastic process in which events occur at a constant rate and independently of each other. In other words, the time between events in a Poisson process is exponentially distributed, and the probability that a given event will occur in a particular time interval is the same for any time interval of the same length.
The probability density function of the Choi-Williams distribution is given by the following equation:
f(x) = λ(1 – e^(-θx))^k – 1
where λ is the rate at which events occur in the Poisson process, θ is the threshold that the process must exceed, and k is the number of events that must occur for the process to be considered to have exceeded the threshold.
As an example, suppose that we are interested in the time it takes for a factory to produce 1000 widgets. If the rate at which the factory produces widgets is 100 widgets per hour, and we consider the process to have exceeded the threshold once it has produced 1000 widgets, we can use the Choi-Williams distribution to model this process. In this case, λ = 100, θ = 1000, and k = 1.
To find the probability that it will take the factory less than 10 hours to produce 1000 widgets, we can use the following equation:
P(x < 10) = ∫_0^10 f(x) dx
To evaluate this integral, we can plug in the values for λ, θ, and k and then use calculus to find the value of the integral. If we do this, we find that the probability that it will take the factory less than 10 hours to produce 1000 widgets is approximately 0.73. This means that there is a 73% chance that the factory will be able to produce 1000 widgets in less than 10 hours.
Overall, the Choi-Williams distribution is a useful tool for modeling the time it takes for a Poisson process to exceed a certain threshold. By understanding the parameters of the distribution and using calculus to evaluate the probability density function, we can make predictions about the time it will take for a process to reach a certain level of intensity.