We characterize the class of exchangeable feature allocations assigning probability Vn,k∏kl=1WmlUn−ml to a feature allocation of n individuals, displaying k features with counts (m1,…,mk) for these features. Each element of this class is parametrized by a countable matrix V and two sequences U and W of nonnegative weights. Moreover, a consistency condition is imposed to guarantee that the distribution for feature allocations of (n−1) individuals is recovered from that of n individuals, when the last individual is integrated out. We prove that the only members of this class satisfying the consistency condition are mixtures of three-parameter Indian buffet Processes over the mass parameter γ, mixtures of N-dimensional Beta–Bernoulli models ov...
We discuss inference for multiple binary sequences under the hypothesis of Markov exchangeability. S...
International audienceA lower bound for the distribution function of a k-dimensional, n-extensible e...
We propose a Bayesian nonparametric prior over feature allocations for sequential data, the birth- d...
We characterize the class of exchangeable feature allocations assigning probability Vn,k∏kl=1WmlUn−m...
Feature allocation models are popular models used in different applications such as unsupervised lea...
Distributions over exchangeable matrices with infinitely many columns, such as the Indian buffet pro...
We define a probability distribution over equivalence classes of binary matrices with a finite numbe...
Clustering involves placing entities into mutually exclusive categories. We wish to relax the requir...
A sequence of random variables is exchangeable if its joint distribution is invariant under variable...
The Indian buffet process (IBP) is an exchangeable distribution over binary matrices used in Bayesia...
Sum-Product Networks (SPNs) are expressive probabilistic models that provide exact, tractable infere...
In this paper we discuss models for multiple binary sequences using de Finetti's notions of exc...
This paper discusses distributions of the composition of a large number of agents by their types, th...
Abstract. A “dispersion ” specifies the relative probability be-tween any two elements of a finite d...
We derive an expression for the joint distribution of exchangeable multinomial random variables, whi...
We discuss inference for multiple binary sequences under the hypothesis of Markov exchangeability. S...
International audienceA lower bound for the distribution function of a k-dimensional, n-extensible e...
We propose a Bayesian nonparametric prior over feature allocations for sequential data, the birth- d...
We characterize the class of exchangeable feature allocations assigning probability Vn,k∏kl=1WmlUn−m...
Feature allocation models are popular models used in different applications such as unsupervised lea...
Distributions over exchangeable matrices with infinitely many columns, such as the Indian buffet pro...
We define a probability distribution over equivalence classes of binary matrices with a finite numbe...
Clustering involves placing entities into mutually exclusive categories. We wish to relax the requir...
A sequence of random variables is exchangeable if its joint distribution is invariant under variable...
The Indian buffet process (IBP) is an exchangeable distribution over binary matrices used in Bayesia...
Sum-Product Networks (SPNs) are expressive probabilistic models that provide exact, tractable infere...
In this paper we discuss models for multiple binary sequences using de Finetti's notions of exc...
This paper discusses distributions of the composition of a large number of agents by their types, th...
Abstract. A “dispersion ” specifies the relative probability be-tween any two elements of a finite d...
We derive an expression for the joint distribution of exchangeable multinomial random variables, whi...
We discuss inference for multiple binary sequences under the hypothesis of Markov exchangeability. S...
International audienceA lower bound for the distribution function of a k-dimensional, n-extensible e...
We propose a Bayesian nonparametric prior over feature allocations for sequential data, the birth- d...