binomial distribution function

Bernoulli, binomial, Beta distribution detailed This paper focuses on the distribution of random variable associated with discrete stochastic variables, discrete random variables continuous random variable such as classic Gaussian distributions

Bata distribution: a random proportion, just as the proportion of defective products in tasks completed within a period of time. Binary: the number of results that appear within the specified number of tests; often used to indicate the success rate

IntroductionI feel that learning machine learning algorithms is the only way to get started from a mathematical perspective, the machine learning field, the machine learning definition given by Michael I Jordan is, "A field that bridge computation

Theshortletdistribution Dirichlet distribution (PRML2.2.1) Dirichlet distribution can be seen as distribution above the distribution. To understand this sentence, let's take an example: Suppose we have a dice with six sides: {1, 2, 3, 4, 5, 6 }. Now

Knowledge Points: Bernoulli distribution, two-item distribution, polynomial distribution, prior probability, posterior probability, conjugate distribution, beta distribution, beta-two-item distribution, negative two-item distribution, Dirichlet

Discrete variable refers to the variable can only take discrete isolated values, usually by the number of units of measurement, such as number, number of units. Many of the distributions of discrete variables are related to last, so let's take a

Exponential family (exponential distribution family) is a common concept, but its definition is not particularly clear, today look at the wiki content, have a general understanding, first and share with you. This article is basically the translation

Dirichlet distributions can be seen as distributions above the distribution. How to understand this sentence, we can give an example: suppose we have a dice, it has six sides, respectively, {1,2,3,4,5,6}. Now we have done 10,000 throw experiments,

In the field of machine learning, probability distribution plays an important role in the understanding of data. Whether it is effective or noisy data, if you know the distribution of data, then in the data modeling process will be a great

Understanding the GAUSSIAN DistributionRandomness is, present in our reality, we are used to take it for granted. The most of the phenomena which surround us has been generated by random processes. Hence, our brain are very good at recognise these