Multinomial distribution - Wikipedia
In probability theory, the multinomial distribution is a generalization of the binomial distribution. For example, it models the probability of counts for each side of a k-sided die rolled n times.
多项分布 - 百度百科
多项式分布（Multinomial Distribution）是二项式分布的推广。二项分布的典型例子是扔硬币，硬币正面朝上概率为p, 重复扔n次硬币，k次为正面的概率即为一个二项分布概率。
【概率论】5-9:多项式分布(The Multinomial Distributions)
Theorem Suppose that the random vector X ⃗ = ( X 1 , X 2 ) \vec{X}=(X_1,X_2) X = (X 1 , X 2 ) has the multinomial distribution with parameters n n n and p ⃗ = ( p 1 , p 2 ) \vec{p}=(p_1,p_2) p = (p 1 , p 2 ).Then X 1 X_1 X 1 has the binomial distribution with parameters n n n and p 1 p_1 p 1 ,and X 2 = n − X 1 X_2=n-X_1 X 2 = n − X 1
Pytorch中的多项分布multinomial.Multinomial().sample()解析
文章详细解释了PyTorch中的Multinomial分布，包括参数total_count、probs和logits的含义及用法，以及sample()函数的抽样机制。 还讨论了validate_args参数对参数检查的影响，指出即使设置为False，仍会进行一定程度的参数验证。
Multinomial theorem - Wikipedia
In mathematics, the multinomial theorem describes how to expand a power of a sum in terms of powers of the terms in that sum. It is the generalization of the binomial theorem from binomials to multinomials .
Multinomial Distribution: Definition, Examples - Statistics How To
The multinomial distribution is used to find probabilities in experiments where there are more than two outcomes. Definition and examples.
R统计学(04): 多项分布 - 知乎 - 知乎专栏
今天我们介绍另一个 离散分布 ：多项分布(Multinomial distribution)，它是二项分布的推广。 1. 特点. 二项分布的试验结果只有两个(成功和失败)，而 多项分布 的试验结果则多于两个。如果试验的结果有三个，则是三项分布；如果结果有六个，则是六项分布。
Multinomial distribution | Properties, proofs, exercises - Statlect
Multinomial distribution. by Marco Taboga, PhD. The multinomial distribution is a multivariate discrete distribution that generalizes the binomial distribution.
Pytorch中的多项分布multinomial.Multinomial().sample()解析
多项分布 Multinomial()是torch.distributions.multinomial中的一个类，接受四个参数(total_count=1, probs=None, logits=None, validate_args=None)： total_count 接受的是 int 型参数，指的是单次抽样中样本的个数。
multinomial (n,p,1−p). On the other hand if (X 1,...,Xk) is multino-mial (n,p 1,...,pk) then for each j, Xj is binomial (n,pj). 2. Simple multinomial hypotheses Suppose we have a simple hypothesis H 0 specifying the πj, namely πj = pj for j = 1,...,k. For example, in rolling a die, there are k =