Posterior sampling from $\varepsilon$-approximation of normalized completely random measure mixtures
نویسندگان
چکیده
منابع مشابه
Slice sampling normalized kernel-weighted completely random measure mixture models
A number of dependent nonparametric processes have been proposed to model non-stationary data with unknown latent dimensionality. However, the inference algorithms are often slow and unwieldy, and are in general highly specific to a given model formulation. In this paper, we describe a large class of dependent nonparametric processes, including several existing models, and present a slice sampl...
متن کاملMCMC for Normalized Random Measure Mixture Models
This paper concerns the use of Markov chain Monte Carlo methods for posterior sampling in Bayesian nonparametric mixture models with normalized random measure priors. Making use of some recent posterior characterizations for the class of normalized random measures, we propose novel Markov chain Monte Carlo methods of both marginal type and conditional type. The proposed marginal samplers are ge...
متن کاملTree-Guided MCMC Inference for Normalized Random Measure Mixture Models
Normalized random measures (NRMs) provide a broad class of discrete random measures that are often used as priors for Bayesian nonparametric models. Dirichlet process is a well-known example of NRMs. Most of posterior inference methods for NRM mixture models rely on MCMC methods since they are easy to implement and their convergence is well studied. However, MCMC often suffers from slow converg...
متن کاملRandom approximation of a general symmetric equation
In this paper, we prove the Hyers-Ulam stability of the symmetric functionalequation $f(ph_1(x,y))=ph_2(f(x), f(y))$ in random normed spaces. As a consequence, weobtain some random stability results in the sense of Hyers-Ulam-Rassias.
متن کاملRandom Sampling from Databases
Random Sampling from Databases by Frank Olken Doctor of Philosophy in Computer Science University of California at Berkeley Professor Michael Stonebraker, Chair In this thesis I describe e cient methods of answering random sampling queries of relational databases, i.e., retrieving random samples of the results of relational queries. I begin with a discussion of the motivation for including samp...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Electronic Journal of Statistics
سال: 2016
ISSN: 1935-7524
DOI: 10.1214/16-ejs1168