How to implement these five types of powerful probability distributions in Python

The probability distributions for the six common distributions are as follows:

1. Discrete distribution: 0-1 distribution.

Only one event trial is performed first, and the probability of the event occurring is p, and the probability of not occurring is 1-p

2. Discrete distribution: geometric distribution.

In n Bernoulli experiments, it took k experiments to obtain the probability of the first success. In detail, it means that the first k-1 times all failed, and the probability of the k-th success.

3. Discrete distribution: binomial distribution.

In the Bernoulli test with n independent repetitions, let the probability of a occur in each trial is p. x is used to denote the number of occurrences of a in the n-weight Bernoulli test, and the probability that event A occurs exactly k times in the n-fold experiment is a binomial distribution.

4. Discrete distribution: Poisson distribution.

The probability that something will happen x times per unit of time.

5. Continuous distribution: uniform distribution.

The probability of obeying a uniformly distributed random variable x on the interval (a,b) falling within any subinterval of equal length in the interval (a,b) is the same.

6. Continuous distribution: exponential distribution.

Describes the probability distribution of the time interval between the occurrence of two random events.

7. Continuous distribution: normal distribution.

The state distribution is the distribution of a continuous random variable with two parameters and 2, the first parameter is the mean of the random variable that follows the normal distribution, and the second parameter 2 is the variance of this random variable, so the normal distribution is denoted as n( ,2 ).

The R programming language has become the de facto standard in statistical analysis. But in this article, I'm going to show you how easy it would be to implement statistical concepts in python. I'm going to use python to implement some discrete and continuous probability distributions.

While I won't go into the mathematical details of these distributions, I'll give you some good information on learning these statistical concepts in the form of links. Before I talk about these probability distributions, I'd like to briefly talk about what a random variable is. A random variable is a quantification of the results of an experiment.

For example, a random variable that represents the outcome of a coin toss can be expressed as.

python

x = A random variable is a variable that takes its value from a set of possible imitation values (discrete or continuous) and obeys some kind of randomness. Each possible value of a random variable is associated with a probability. All possible values of a variable and the probability associated with it are called probability distributrions.

I encourage everyone to take a closer look at the modules.

There are two types of probability distributions: discrete probability distributions and continuous probability distributions.

Discrete probability distributions are also known as probability mass functions. Examples of discrete probability distributions include Bernoulli distribution, binomial distribution, Poisson distribution, and geometric distribution.

Continuous probability distributions, also known as probability density functions, are functions that have a continuous value, such as the value on a solid line. Normal distribution, exponential distribution, and beta distribution are all continuous probability distributions.