相关论文: (H)DPGMM: A Hierarchy of Dirichlet Process Gaussia…
Black hole population studies are currently performed either using astrophysically motivated models (informed but rigid in their functional forms) or via non-parametric methods (flexible but not directly interpretable). In this paper, we…
We analyze the population statistics of black holes in the LIGO/Virgo/KAGRA GWTC-3 catalog using a parametric mass function derived from simulations of massive stars experiencing pulsational pair-instability supernovae (PPISN). Our…
Hierarchical mergers of black holes are proposed as a mechanism to explain the observations of binary black holes with component masses between $\sim 50M_{\odot}\hbox{--}130M_{\odot}$ by LIGO/Virgo, often referred to as "upper mass gap". We…
Gravitational-wave (GW) measurements of physical effects such as spin-induced quadrupole moments can distinguish binaries consisting of black holes from non-black hole binaries. While these effects may be poorly constrained for single-event…
We analyze binary black hole (BBH) mergers from the latest Gravitational Wave Transient Catalog (GWTC-3) using a flexible, non-parametric framework to infer the underlying black hole mass distribution. Our model employs Gaussian Processes…
In social science research, understanding latent structures in populations through survey data with categorical responses is a common and important task. Traditional methods like Factor Analysis and Latent Class Analysis have limitations,…
Dirichlet process mixture model (DPMM) is a popular Bayesian nonparametric model. In this paper, we apply this model to weighted data and then estimate the un-weighted distribution from the corresponding weighted distribution using the…
We develop a new method based on Gaussian process to reconstruct the mass distribution of binary black holes (BBHs). Instead of prespecifying the formalisms of mass distribution, we introduce a more flexible and nonparametric model with…
Gravitational waves (GWs) signals detected by the LIGO/Virgo/KAGRA collaboration might be sourced (partly) by the merges of primordial black holes (PBHs). The conventional hierarchical Bayesian inference methods can allow us to study…
The parsimonious Gaussian mixture models, which exploit an eigenvalue decomposition of the group covariance matrices of the Gaussian mixture, have shown their success in particular in cluster analysis. Their estimation is in general…
Employing nonparametric methods for density estimation has become routine in Bayesian statistical practice. Models based on discrete nonparametric priors such as Dirichlet Process Mixture (DPM) models are very attractive choices due to…
Current gravitational-wave data reveal structures in the mass function of binary compact objects. Properly modelling and deciphering such structures is the ultimate goal of gravitational-wave population analysis: in this context,…
Rapidly growing catalogs of compact binary mergers from advanced gravitational-wave detectors allow us to explore the astrophysics of massive stellar binaries. Merger observations can constrain the uncertain parameters that describe the…
From the formation mechanisms of stars and compact objects to nuclear physics, modern astronomy frequently leverages surveys to understand populations of objects to answer fundamental questions. The population of dark and isolated compact…
A natural Bayesian approach for mixture models with an unknown number of components is to take the usual finite mixture model with Dirichlet weights, and put a prior on the number of components---that is, to use a mixture of finite mixtures…
This paper presents a novel algorithm, based upon the dependent Dirichlet process mixture model (DDPMM), for clustering batch-sequential data containing an unknown number of evolving clusters. The algorithm is derived via a low-variance…
We present the \textit{hierarchical Dirichlet scaling process} (HDSP), a Bayesian nonparametric mixed membership model. The HDSP generalizes the hierarchical Dirichlet process (HDP) to model the correlation structure between metadata in the…
Bayesian nonparametric mixture models are common for modeling complex data. While these models are well-suited for density estimation, recent results proved posterior inconsistency of the number of clusters when the true number of…
The catalog of gravitational-wave events is growing, and so are our hopes of constraining the underlying astrophysics of stellar-mass black-hole mergers by inferring the distributions of, e.g., masses and spins. While conventional analyses…
We propose Dirichlet Process mixtures of Generalized Linear Models (DP-GLM), a new method of nonparametric regression that accommodates continuous and categorical inputs, and responses that can be modeled by a generalized linear model. We…