2016年11月9日 · $\begingroup$ The Dirichlet distribution is a distribution over the simplex, hence a distribution over finite support distributions. If you aim at a distribution over continuous distributions, you should look at the Dirichlet process. $\endgroup$ –

2019年1月16日 · 1) Don't get caught up in the technical details too much. I will focus on symmetric Dirichlet forms because I think the situation is somewhat more transparent in this case (the coercivity condition is automatically satisfied for symmetric forms). Condition 2 is there to ensure the existence of a generator of the form.

2014年12月25日 · Some of these are generalizations of other distributions hence, including such as Dirichlet, which is a generalization on the Beta distribution, i.e. Dirichlet generalized the Beta into multiple dimensions. For this reason and so many others, Dirichlet distribution is the Conjugate Prior for Multinomial Distribution. Now back to our SNPs problem:

2015年10月3日 · And what is the purpose of that double sum — is taking a limit of that the very definition of Dirichlet regularization of the series $\sum_{n=1}^{\infty} f(n)$? Please help me by being precise and even prolix in your answer! $\endgroup$ –

2016年4月28日 · The uniform distribution is actually a special case of the Dirichlet distribution, corresponding to the case $\alpha_1=\alpha_2=\cdots=\alpha_k=1$. So is the least-informative Jeffreys prior, for which $\alpha_1=\cdots=\alpha_k=1/2$. The fact that the Dirichlet class includes these natural "non-informative" priors is another reason for using it.

Construction of Dirichlet distribution with Gamma distribution. Ask Question Asked 12 years, 4 months ago.

2015年8月16日 · The Dirichlet process is a prior over distributions. Informally, you thrown in a probability distribution and when you sample from it, out you will get probability distribution after probability distribution.

2019年6月20日 · That is the definition of the Dirichlet (semi)norm, taken as an analogue of the corresponding concept from ...

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2020年5月21日 · Both Dirichlet and multinomial distributions are distributions over vectors, and both Dirichlet and multinomial distributions are constrained so that all of the elements of these vectors sum to a constant value. Can somebody explain in simple words (and maybe with an example)in-detailed differences between Dirichlet and multinomial distributions?