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Dirichlet parameters add under coarsening
Statement
Assume AC. Let have the Dirichlet distribution with positive parameters . If is a partition of into nonempty groups, then When , the grouped vector is the point mass at .
Facts & Assumptions
Given: Full AC; positive ; and a partition of the coordinate indices into nonempty groups.
Full AC is assumed. The Axiom of Choice.
Full AC implies the Countable Choice required by Gamma density normalization. AC implies DC implies countable choice The Axiom of Countable Choice ().
On a finite discrete standard Borel space, the normalized-gamma construction realizes a positive-parameter Dirichlet vector as independent increments divided by their sum. Existence of a Dirichlet process by normalized gamma Poisson random measure.
Under Countable Choice, a shape-rate Gamma variable has density , normalized for ; the Euler Gamma value is finite and positive. Shape-rate Gamma distribution The real Gamma function by Euler's integral Euler's Gamma integral converges exactly for positive real parameters.
Under Countable Choice, the nonnegative Lebesgue change-of-variables formula holds for diffeomorphisms. A C^1 diffeomorphism satisfies the change-of-variables formula for nonnegative Lebesgue measurable functions.
Finite measures with an exponential moment and the same Laplace transform on a neighborhood of zero are equal under AC. Uniqueness of a multivariate exponential transform.
Finite products of probability measures exist and satisfy the rectangle product formula. For sigma-finite factors, the product measure exists, has the rectangle formula, is sigma-finite, and is unique.
The finite-dimensional Dirichlet law is the positive-parameter simplex law specified in Dirichlet distribution, including the one-category case.
Every nonempty finite discrete space is standard Borel. Dirac set functions are probability measures; finite nonnegative weighted sums of them are measures. A countable discrete measurable space is standard Borel A Dirac set function is a probability measure Nonnegative scalar multiples and countable weighted sums of measures are measures.
Proof
Given: The hypotheses in the Statement.
The nonempty finite discrete space is standard Borel by [F7]. Define for , equivalently . By [F7] this is a measure, with finite positive total , so [F1] applies to . Thus the law of is that of , where the are independent variables, and it is the Dirichlet law [F6].
For , [F2] and the change of variables in [F3] give . For , independence therefore gives the joint moment-generating function for , since the groups use disjoint coordinates in the finite product law [F5].
By [F5], the product law of independent variables exists and has the same joint transform. Both laws have finite exponential moment at every , since the transform at is finite. Applying [F4] to the difference of these two finite measures shows that has that product Gamma law.
Normalize the grouped variables by their common positive total. The resulting vector is Dirichlet with parameters by [F1], while by step 1.1. This proves the claim for . For the only coordinate sum is , which is the stated one-category law.
Depends on
- A countable discrete measurable space is standard Borel
- A Dirac set function is a probability measure
- Nonnegative scalar multiples and countable weighted sums of measures are measures
- Existence of a Dirichlet process by normalized gamma Poisson random measure
- Dirichlet distribution, including the one-category case
- Shape-rate Gamma distribution
- The Axiom of Choice
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
- AC implies DC implies countable choice
- Uniqueness of a multivariate exponential transform
- For sigma-finite factors, the product measure exists, has the rectangle formula, is sigma-finite, and is unique
- A C^1 diffeomorphism satisfies the change-of-variables formula for nonnegative Lebesgue measurable functions
- The real Gamma function by Euler's integral
- Euler's Gamma integral converges exactly for positive real parameters
Used by
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Sources
- Thomas S. Ferguson, A Bayesian Analysis of Some Nonparametric Problems, 1973, Section 2 (standard reference, not scraped)
- Günter Last and Mathew Penrose, Lectures on the Poisson Process, 2017, Chapter 1 (standard reference, not scraped)