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Weak Convergence of a Dirichlet-Multinomial Process
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Pietro Muliere
Published/Copyright:
March 3, 2010
Abstract
We present a random probability distribution which approximates, in the sense of weak convergence, the Dirichlet process and supports a Bayesian resampling plan called a proper Bayesian bootstrap.
Key words and phrases:: Dirichlet process; proper Bayesian bootstrap
Received: 2002-10-03
Revised: 2003-01-23
Published Online: 2010-03-03
Published in Print: 2003-June
© Heldermann Verlag
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Articles in the same Issue
- On ARU-Resolutions of Uniform Spaces
- On the Dimension Modulo Classes of Topological Spaces and Free Topological Groups
- On the Uniform Convergence and 𝐿-Convergence of Double Fourier Series with Respect to the Walsh–Kaczmarz System
- Dynamic Programming and Mean-Variance Hedging in Discrete Time
- Extending Quasi-Invariant Measures by Using Subgroups of a Given Group
- Representation of Solutions of Some Boundary Value Problems of Elasticity by a Sum of the Solutions of Other Boundary Value Problems
- General 𝑀-Estimators in the Presence of Nuisance Parameters. Skew Projection Technique
- A Unified Characterization of 𝑞-Optimal and Minimal Entropy Martingale Measures by Semimartingale Backward Equations
- On the Existence of Continuous Modifications of Vector-Valued Random Fields
- Weak Convergence of a Dirichlet-Multinomial Process
- Time-Dependent Barrier Options and Boundary Crossing Probabilities
- On Separation Properties for Families of Probability Measures
- Oscillation Theorems of Nonlinear Difference Equations of Second Order
- On Bounds for the Characteristic Functions of Some Degenerate Multidimensional Distributions
- Estimates of Fourier Coefficients
- On Approximate Large Deviations for 1D Diffusion
