1.8k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of ViennaAT14 papers
- Sapienza University of RomeIT12 papers
- Complexity Science HubAT11 papers
- University of SalzburgAT9 papers
- Johannes Kepler University of LinzAT8 papers
- Humboldt-Universität zu BerlinDE7 papers
- The University of MelbourneAU7 papers
- TU WienAT7 papers
- ETH ZurichCH6 papers
- Queensland University of TechnologyAU5 papers
- University College DublinIE5 papers
- University of AmsterdamNL5 papers
21 papers · 1 filter
Marginal Data Augmentation for Efficient Bayesian Modeling of Counts and Rates with a Demographic Application
Gregor Zens, Sylvia Frühwirth-Schnatter
Count data models are ubiquitous in many fields, yet Bayesian data augmentation algorithms for such models frequently encounter challenges with Markov chain Monte Carlo efficiency.…
Seeded Poisson Factorization: leveraging domain knowledge to fit topic models
Bernd Prostmaier, Jan Vávra, Bettina Grün +1
Topic models are widely used for discovering latent thematic structures in large text corpora, yet traditional unsupervised methods often struggle to align with pre-defined concept…
A Structural Text-Based Scaling Model for Analyzing Political Discourse
Jan Vávra, Bernd Hans-Konrad Prostmaier, Bettina Grün +1
Scaling political actors based on their individual characteristics and behavior helps profiling and grouping them as well as understanding changes in the political landscape. In th…
Evolving Voices Based on Temporal Poisson Factorisation
Jan Vávra, Bettina Grün, Paul Hofmarcher
The world is evolving and so is the vocabulary used to discuss topics in speech. Analysing political speech data from more than 30 years requires the use of flexible topic models t…
Bayesian Finite Mixture Models
Bettina Grün, Gertraud Malsiner-Walli
Finite mixture models are a useful statistical model class for clustering and density approximation. In the Bayesian framework finite mixture models require the specification of su…
Generalized Cumulative Shrinkage Process Priors with Applications to Sparse Bayesian Factor Analysis
Sylvia Frühwirth-Schnatter
The paper discusses shrinkage priors which impose increasing shrinkage in a sequence of parameters. We review the cumulative shrinkage process (CUSP) prior of Legramanti et al. (20…
When it counts -- Econometric identification of the basic factor model based on GLT structures
Sylvia Frühwirth-Schnatter, Darjus Hosszejni, Hedibert Freitas Lopes
Despite the popularity of factor models with sparse loading matrices, little attention has been given to formally address identifiability of these models beyond standard rotation-b…
Deep Quantile and Deep Composite Model Regression
Tobias Fissler, Michael Merz, Mario V. Wüthrich
A main difficulty in actuarial claim size modeling is that there is no simple off-the-shelf distribution that simultaneously provides a good distributional model for the main body…
Spying on the prior of the number of data clusters and the partition distribution in Bayesian cluster analysis
Jan Greve, Bettina Grün, Gertraud Malsiner-Walli +1
Cluster analysis aims at partitioning data into groups or clusters. In applications, it is common to deal with problems where the number of clusters is unknown. Bayesian mixture mo…
Generalized mixtures of finite mixtures and telescoping sampling
Sylvia Frühwirth-Schnatter, Gertraud Malsiner-Walli, Bettina Grün
Within a Bayesian framework, a comprehensive investigation of mixtures of finite mixtures (MFMs), i.e., finite mixtures with a prior on the number of components, is performed. This…
Discussion on Bayesian Cluster Analysis: Point Estimation and Credible Balls by Sara Wade and Zoubin Ghahramani
William Weimin Yoo
I begin my discussion by giving an overview of the main results. Then I proceed to touch upon issues about whether the credible ball constructed can be interpreted as a confidence…
Exact integrated completed likelihood maximisation in a stochastic block transition model for dynamic networks
Riccardo Rastelli
The latent stochastic block model is a flexible and widely used statistical model for the analysis of network data. Extensions of this model to a dynamic context often fail to capt…
Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models
Gregor Kastner, Sylvia Frühwirth-Schnatter
Bayesian inference for stochastic volatility models using MCMC methods highly depends on actual parameter values in terms of sampling efficiency. While draws from the posterior uti…
Effect fusion using model-based clustering
Gertraud Malsiner-Walli, Daniela Pauger, Helga Wagner
In social and economic studies many of the collected variables are measured on a nominal scale, often with a large number of categories. The definition of categories is usually not…
Sparse Bayesian time-varying covariance estimation in many dimensions
Gregor Kastner
We address the curse of dimensionality in dynamic covariance estimation by modeling the underlying co-volatility dynamics of a time series vector through latent time-varying stocha…
Should I stay or should I go? A latent threshold approach to large-scale mixture innovation models
Florian Huber, Gregor Kastner, Martin Feldkircher
This paper proposes a straightforward algorithm to carry out inference in large time-varying parameter vector autoregressions (TVP-VARs) with mixture innovation components for each…
Optimal Bayesian estimators for latent variable cluster models
Riccardo Rastelli, Nial Friel
In cluster analysis interest lies in probabilistically capturing partitions of individuals, items or observations into groups, such that those belonging to the same group share sim…
Model-based clustering based on sparse finite Gaussian mixtures
Gertraud Malsiner-Walli, Sylvia Frühwirth-Schnatter, Bettina Grün
In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian distributions, we present a joint approach to estimate the number of mixture components an…
Fitting High-Dimensional Interaction Models with Error Control
Kory D. Johnson, Robert A. Stine, Dean P. Foster
There is a renewed interest in polynomial regression in the form of identifying influential interactions between features. In many settings, this takes place in a high-dimensional…
Bayesian cluster analysis: Point estimation and credible balls
Sara Wade, Zoubin Ghahramani
Clustering is widely studied in statistics and machine learning, with applications in a variety of fields. As opposed to classical algorithms which return a single clustering solut…
Identifying Mixtures of Mixtures Using Bayesian Estimation
Gertraud Malsiner-Walli, Sylvia Frühwirth-Schnatter, Bettina Grün
The use of a finite mixture of normal distributions in model-based clustering allows to capture non-Gaussian data clusters. However, identifying the clusters from the normal compon…