Bayesian latent variable models for mixed discrete outcomes by Dunson D.B., Herring A.H.

By Dunson D.B., Herring A.H.

Show description

Read or Download Bayesian latent variable models for mixed discrete outcomes PDF

Best probability books

Probabilistic Theory of Structures

Well-written creation covers likelihood idea from or extra random variables, reliability of such multivariable buildings, idea of random functionality, Monte Carlo tools for difficulties incapable of actual resolution, extra.

Log Linear Models and Logistic Regression

This booklet examines statistical versions for frequency information. the first concentration is on log-linear types for contingency tables,but during this moment edition,greater emphasis has been put on logistic regression. themes resembling logistic discrimination and generalized linear types also are explored. The remedy is designed for college students with past wisdom of study of variance and regression.

An Introduction to Structured Population Dynamics

Curiosity within the temporal fluctuations of organic populations will be traced to the sunrise of civilization. How can arithmetic be used to realize an knowing of inhabitants dynamics? This monograph introduces the speculation of dependent inhabitants dynamics and its purposes, concentrating on the asymptotic dynamics of deterministic types.

Additional info for Bayesian latent variable models for mixed discrete outcomes

Sample text

We will use the notation Fn(z, ώ) for a random algebraic polynomial of degree n. 22 2. 3. 2 random algebraic polynomials were defined via random power series. We now give some other definitions of random algebraic polynomials, several of which are based on notions from probabilistic functional analysis. , αη(ω)): Ω -► (R„+i, (B(R„+i)). To be more precise, Kac [10] has considered the following "model" for a random algebraic polynomial. Given a deterministic algebraic polynomial of degree n with real coefficients we associate the point a = (#o, #i, ·.

The following result is due to Arnold [1]. 3. Let F„(z, ω), z e D, be a random algebraic polynomial of degree n, and let ΐίί = [B : B C D, Nn(B, ώ) is a random variable}. Then (R(D) C 91. 26 2. Basic Definitions and Properties We will need the following lemma. 1. Let hn and h be holomorphic functions on D, with hn converging to h ^ 0 uniformly on every compact subset of D. If Nn and N are the number of zeros ofhn and Λ, then Nn converges vaguely^ to N. Proof. Let A be a continuity set of N. Since the zeros of h in A are interior points of A, whereas all other zeros of h are exterior points, it follows from Hurwitz's theorem* that Nn(A) = N(A) for all n ^ no.

3 is devoted to estimates to £>[Nn(B, ω)} when the coefficients of 49 50 4. 1) are real-valued random variables. 1) are complex-valued random variables. 2. ESTIMATES OF Nn(B, ω) A. Introduction Consider a random algebraic polynomial Fn(z, ω) of degree n. 2B we define the upper and lower bounds of Nn(B, ω), and present some estimates when the coefficients are normally distributed. These estimates are given in terms of probability measure, since Nn(B, ώ) is a random variable for every fixed B e (B(£>).

Download PDF sample

Rated 4.24 of 5 – based on 7 votes