Research any topic before you write.
Find related topics. | Discover entities. | See connections. | Build a topical map.
In probability theory and statistics, the Dirichlet-multinomial distribution is a family of discrete multivariate probability distributions on a finite support of non-negative integers. It is also called the Dirichlet compound multinomial distribution (DCM) or multivariate Pólya distribution (after George Pólya). It is a compound probability…
Applications & Products
Explore the main themes, entities and connections around Dirichlet-multinomial distribution. Start with the topic map, then use the sections below for research and deeper semantic analysis.
Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the full topic structure. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
See the strongest relationship patterns around the current topic before diving into the raw triples.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
distribution displaystyle categorical variables dirichlet multinomial conditional dependent probability joint dirichlet-multinomial alpha variable model word words number given topic case
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Dirichlet-multinomial distribution | CF | E ( ∏ k = 1 K e i t k ⋅ x k ) = Γ ( α 0 ) Γ ( n + 1 ) Γ ( n + α 0 ) ⋅ D n ( α , ( e i t 1 , . . . , e i t K ) ) {\displaystyle \operatorname {E} (\prod \limits _{k=1}^{K}{e}^{… | 1.00 | infobox |
| Dirichlet-multinomial distribution | Mean | E ( X i ) = n α i α 0 {\displaystyle \operatorname {E} (X_{i})=n{\frac {\alpha _{i}}{\alpha _{0}}}} | 1.00 | infobox |
| Dirichlet-multinomial distribution | MGF | E ( ∏ k = 1 K e t k ⋅ x k ) = Γ ( α 0 ) Γ ( n + 1 ) Γ ( n + α 0 ) ⋅ D n ( α , ( e t 1 , . . . , e t K ) ) {\displaystyle \operatorname {E} (\prod \limits _{k=1}^{K}{e}^{t_{k}\… | 1.00 | infobox |
| Dirichlet-multinomial distribution | Notation | D i r M u l t ( n , α ) {\displaystyle \mathrm {DirMult} (n,{\boldsymbol {\alpha }})} | 1.00 | infobox |
| Dirichlet-multinomial distribution | Parameters | n ∈ { 0 , 1 , 2 , … } {\displaystyle n\in \{0,1,2,\ldots \}} number of trials α 1 , … , α K > 0 , α 0 = ∑ α k {\displaystyle \alpha _{1},\ldots ,\alpha _{K}>0,\alpha _{0}=\sum \… | 1.00 | infobox |
| Dirichlet-multinomial distribution | PGF | E ( ∏ k = 1 K z k x k ) = Γ ( α 0 ) Γ ( n + 1 ) Γ ( n + α 0 ) ⋅ D n ( α , z ) {\displaystyle \operatorname {E} (\prod \limits _{k=1}^{K}{z_{k}}^{x_{k}})={\frac {\Gamma (\alpha… | 1.00 | infobox |
| Dirichlet-multinomial distribution | PMF | Γ ( α 0 ) Γ ( n + 1 ) Γ ( n + α 0 ) ∏ k = 1 K Γ ( x k + α k ) Γ ( α k ) Γ ( x k + 1 ) {\displaystyle {\frac {\Gamma \left(\alpha _{0}\right)\Gamma \left(n+1\right)}{\Gamma \left… | 1.00 | infobox |
| Dirichlet-multinomial distribution | Support | x i ∈ { 0 , … , n } {\displaystyle x_{i}\in \{0,\dots ,n\}} Σ x i = n , 1 ≤ i ≤ K {\displaystyle \Sigma x_{i}=n\!,1\leq i\leq K} | 1.00 | infobox |
| Dirichlet-multinomial distribution | Variance | Var ( X i ) = n α i α 0 ( 1 − α i α 0 ) ( n + α 0 1 + α 0 ) {\displaystyle \operatorname {Var} (X_{i})=n{\frac {\alpha _{i}}{\alpha _{0}}}\left(1-{\frac {\alpha _{i}}{\alpha _… | 1.00 | infobox |
| Dirichlet-multinomial distribution | is a | family of discrete multivariate probability distributions on a finite support of non-negative integers | 0.90 | text |
| Dirichlet-multinomial distribution | is a | set | 0.90 | text |
| the one in this model | instance of | the same simplification would apply in a larger joint probability expression | 0.80 | text |
These clusters group vocabulary that occurs around closely connected concepts in the source material.
Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.