As the Poisson distribution is a discrete function, the variable can only take specific values in a (potentially infinite) list. Put differently, the variable cannot take all values in any ...

A discrete random variable is a type of random variable that can take on a countable set of distinct values. Common examples include the number of children in a family, the outcome of rolling a die, ...

Clustering on uncertain data, one of the essential tasks in mining uncertain data, posts significant challenges on both modeling similarity between uncertain objects and developing efficient ...

The mean, or expected value, of a binomial distribution is fairly straightforward to calculate. It's simply the product of the number of trials (n) and the probability of success (p) in each trial.

Probability is a measure of the likelihood of an event occurring, and it plays a significant role in many fields such as statistics, mathematics, and science. Understanding how to calculate ...

1) Identify the distribution type: Based on your data, choose the most appropriate continuous probability distribution (Normal, Uniform, Exponential, etc.). 2) Determine the parameters: Calculate the ...

A new one-parameter discrete distribution, namely discrete Burr-Hatke distribution is introduced and its mathematical properties are studied comprehensively. The main properties of the discrete ...

3 key takeaways A probability distribution shows the likelihood of different outcomes for a random variable. There are two main types of probability distributions: discrete and continuous ...

Some of the widely adopted probability distributions are discussed below: Bernoulli Distribution The Bernoulli distribution is the most basic discrete distribution, and it serves as a foundation for ...

Image source In the above image, we can see a representation of the probability density function of a normal distribution. The above-given types are the two main types of probability distribution.