36,199 research outputs found
A unifying representation for a class of dependent random measures
We present a general construction for dependent random measures based on
thinning Poisson processes on an augmented space. The framework is not
restricted to dependent versions of a specific nonparametric model, but can be
applied to all models that can be represented using completely random measures.
Several existing dependent random measures can be seen as specific cases of
this framework. Interesting properties of the resulting measures are derived
and the efficacy of the framework is demonstrated by constructing a
covariate-dependent latent feature model and topic model that obtain superior
predictive performance
Non-parametric Bayesian modeling of complex networks
Modeling structure in complex networks using Bayesian non-parametrics makes
it possible to specify flexible model structures and infer the adequate model
complexity from the observed data. This paper provides a gentle introduction to
non-parametric Bayesian modeling of complex networks: Using an infinite mixture
model as running example we go through the steps of deriving the model as an
infinite limit of a finite parametric model, inferring the model parameters by
Markov chain Monte Carlo, and checking the model's fit and predictive
performance. We explain how advanced non-parametric models for complex networks
can be derived and point out relevant literature
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