# Questions tagged [nonparametric-bayes]

Bayesian methods for infinite dimensional parameter spaces.

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### Why are discrete random measures not dominated?

A statement I’ve seen without proof in many books and papers is that random probability measures obtained by normalizing a completely random measure, such as the Dirichlet process, are not dominated ...
231 views

### Bayesian Nonparametric Latent feature model

For quite a long time I've been trying to understand the paper "Bayesian Nonparametric Latent feature model" (by Zoubin Ghahramani et al.) [http://mlg.eng.cam.ac.uk/zoubin/papers/GhaGriSol06.pdf]. In ...
35 views

### Exchangabilty in CRF generative model for HDP

In the HDP Setting, the groups (or documents) are assumed exchangeable between them, and the samples (or words) within each group/topic are also exchangeable. Note the Setting chapter here However, ...
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### Kernel density estimate vs Dirichlet process mixture

Nowadays the Dirichlet process mixture (DPM) seems to be the default Bayesian approach for density estimation. My question is why not simply use the kernel density estimate (KDE) to model the density? ...
87 views

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### In a Dirichlet process, can the base distribution be discrete?

Must it be continuous? Note we are talking about the base distribution. The sampled distribution is discrete. 1) If the base distribution is continuous, drawing from it will get a new value (a new ...
395 views

### Help me understand the Bayesian kernel density estimation (Sibisi and Skilling, 1996)

Sibisi and Skilling (1996, also mentioned in the 1997 paper) define Bayesian kernel density as $$f(x) = \int dx' \,\phi(x')\, K(x, x') \tag{2}$$ Here the kernel $K$ is an assigned smooth ...
1k views

### Normality test for likert scale [duplicate]

I have a questionnaire data that consist of composite Likert scale and discrete Likert items. Likert scales or variables are the sum of few Likert items while the one-item variables are represented ...
182 views

### Dirichlet Process vs. Mixture Models with Many Mixtures

The Dirichlet Process prior is a Bayesian non-parametric prior to model your data as coming from an infinite mixture of distributions. Since your data is finite, only a finite number of these mixture ...
33 views

### DPMM asymptotics for finite mixtures?

My understanding is that using a Dirichlet process mixture model for a mixture with finitely many components will result in a misspecified model. Are there any asymptotic results or bounds on the ...
398 views

### Gibbs sampling in the Hierarchical Dirichlet Process

For an inference problem using a Dirichlet Process prior, one can derive a "basic" Gibbs sampling scheme, where we have a conditional for any parameter $\theta_i$ given the samples $x_i$ and all the ...
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### Gaussian Processes: How to use GPML for multi-dimensional output

Is there a way to perform Gaussian Process Regression on multidimensional output (possibly correlated) using GPML? In the demo script I could only find a 1D example. A similar question on CV that ...
180 views

### In GPML, why does scaler in Squared Exponent covariance function gets very small?

I'm running regression using GPML with a covariance function being a sum of a Gaussian noise and a Squared Exponent (SE). Input is in R4 and both the input and the output are normalized. I run ...