• Corpus ID: 236984789

Hierarchical Infinite Relational Model

@inproceedings{Saad2021HierarchicalIR,
  title={Hierarchical Infinite Relational Model},
  author={Feras A. Saad and Vikash K. Mansinghka},
  booktitle={UAI},
  year={2021}
}
This paper describes the hierarchical infinite relational model (HIRM), a new probabilistic generative model for noisy, sparse, and heterogeneous relational data. Given a set of relations defined over a collection of domains, the model first infers multiple non-overlapping clusters of relations using a top-level Chinese restaurant process. Within each cluster of relations, a Dirichlet process mixture is then used to partition the domain entities and model the probability distribution of… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 59 REFERENCES
An Extension of the Infinite Relational Model Incorporating Interaction between Objects
TLDR
This paper proposes an extension of the IRM by introducing a subset mechanism that selects a part of the data according to the interaction among objects and presents posterior probabilities for running collapsed Gibbs sampling to learn the model from the given data.
Modelling Relational Data using Bayesian Clustered Tensor Factorization
TLDR
The Bayesian Clustered Tensor Factorization (BCTF) model is introduced, which embeds a factorized representation of relations in a nonparametric Bayesian clustering framework that is fully Bayesian but scales well to large data sets.
Subset Infinite Relational Models
TLDR
A new probabilistic generative model for analyzing sparse and noisy pairwise relational data, such as friend-links on social network services and customer records in online shops, is proposed that can extract clusters with stronger relations among data within the cluster than clusters obtained by the conventional model.
Infinite Hidden Relational Models
TLDR
This paper presents a relational model, which is completely symmetrical, that introduces for each entity (or object) an infinite-dimensional latent variable as part of a Dirichlet process (DP) model, based on a DP Gibbs sampler.
Efficient Online Inference for Bayesian Nonparametric Relational Models
TLDR
A new model for large social networks, the hierarchical Dirichlet process relational model, which allows nodes to have mixed membership in an unbounded set of communities is introduced, and an online stochastic variational inference algorithm is derived to allow scalable learning.
Poisson Process Infinite Relational Model : a Bayesian nonparametric model for transactional data
TLDR
A Bayesian nonparametric model for discovering latent class-structure in transactional data that derives and elaborates on the necessary procedures to implement a fully Bayesian approximate inference scheme is explored.
Fast Inference in Infinite Hidden Relational Models
TLDR
A natural outcome of the infinite hidden relational model (IHRM) is a clustering of the entities providing interesting insight into the structure of the domain by simply letting the number of hidden states for each entity class approach infinity.
The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies
TLDR
An application to information retrieval in which documents are modeled as paths down a random tree, and the preferential attachment dynamics of the nCRP leads to clustering of documents according to sharing of topics at multiple levels of abstraction.
Bayesian Nonparametric Relational Topic Model through Dependent Gamma Processes
TLDR
A nonparametric relational topic model using stochastic processes instead of fixed-dimensional probability distributions is proposed in this paper, which can discover the hidden topics and its number simultaneously.
Discriminative Probabilistic Models for Relational Data
TLDR
An alternative framework that builds on (conditional) Markov networks and addresses two limitations of the previous approach is presented, showing how to train these models effectively, and how to use approximate probabilistic inference over the learned model for collective classification of multiple related entities.
...
1
2
3
4
5
...