862 research outputs found
Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling
The beta-negative binomial process (BNBP), an integer-valued stochastic
process, is employed to partition a count vector into a latent random count
matrix. As the marginal probability distribution of the BNBP that governs the
exchangeable random partitions of grouped data has not yet been developed,
current inference for the BNBP has to truncate the number of atoms of the beta
process. This paper introduces an exchangeable partition probability function
to explicitly describe how the BNBP clusters the data points of each group into
a random number of exchangeable partitions, which are shared across all the
groups. A fully collapsed Gibbs sampler is developed for the BNBP, leading to a
novel nonparametric Bayesian topic model that is distinct from existing ones,
with simple implementation, fast convergence, good mixing, and state-of-the-art
predictive performance.Comment: in Neural Information Processing Systems (NIPS) 2014. 9 pages + 3
page appendi
Variational Inference in Nonconjugate Models
Mean-field variational methods are widely used for approximate posterior
inference in many probabilistic models. In a typical application, mean-field
methods approximately compute the posterior with a coordinate-ascent
optimization algorithm. When the model is conditionally conjugate, the
coordinate updates are easily derived and in closed form. However, many models
of interest---like the correlated topic model and Bayesian logistic
regression---are nonconjuate. In these models, mean-field methods cannot be
directly applied and practitioners have had to develop variational algorithms
on a case-by-case basis. In this paper, we develop two generic methods for
nonconjugate models, Laplace variational inference and delta method variational
inference. Our methods have several advantages: they allow for easily derived
variational algorithms with a wide class of nonconjugate models; they extend
and unify some of the existing algorithms that have been derived for specific
models; and they work well on real-world datasets. We studied our methods on
the correlated topic model, Bayesian logistic regression, and hierarchical
Bayesian logistic regression
On Spectra of Linearized Operators for Keller-Segel Models of Chemotaxis
We consider the phenomenon of collapse in the critical Keller-Segel equation
(KS) which models chemotactic aggregation of micro-organisms underlying many
social activities, e.g. fruiting body development and biofilm formation. Also
KS describes the collapse of a gas of self-gravitating Brownian particles. We
find the fluctuation spectrum around the collapsing family of steady states for
these equations, which is instrumental in derivation of the critical collapse
law. To this end we develop a rigorous version of the method of matched
asymptotics for the spectral analysis of a class of second order differential
operators containing the linearized Keller-Segel operators (and as we argue
linearized operators appearing in nonlinear evolution problems). We explain how
the results we obtain are used to derive the critical collapse law, as well as
for proving its stability.Comment: 22 pages, 1 figur
Latter research on Euler-Mascheroni constant
In this work, we present a review and an example on some latter results on
the problem of approximating the Euler-Mascheroni constant. We use the method
firstly introduced in [C. Mortici, Product Approximations via Asymptotic
Integration Amer. Math. Monthly 117 (5) (2010) 434-441].Comment: 8 page
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