A Dirichlet process mixture model for marked Poisson point processes with squared-link intensities and Laplace variational inference jointly infers clusters, cluster count, and continuous mark-specific intensity surfaces.
citation dossier
Analysis of professional basketball field goal attempts via a bayesian matrix clustering approach.Journal of Computational and Graphical Statistics, 32(1):49–60
4Pith papers citing it
4reference links
stat.MEtop field · 3 papers
UNVERDICTEDtop verdict bucket · 4 papers
why this work matters in Pith
Pith has found this work in 4 reviewed papers. Its strongest current cluster is stat.ME (3 papers). The largest review-status bucket among citing papers is UNVERDICTED (4 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
years
2026 4verdicts
UNVERDICTED 4representative citing papers
A Bayesian model for multi-feature contact matrices that uses tensor structures and contingency table theory to satisfy structural constraints and impute missing contact features, validated on simulations and US/German survey data.
Principal Nested Cones is a nonlinear dimension reduction technique that projects cone-structured data onto nested lower-dimensional cones to jointly represent size and shape variation.
A review summarizing definitions, canonical forms, exact and approximate distributions, numerical methods, applications, and open problems for quadratic forms in real and complex Gaussian variables, including multiforms and ratios.
citing papers explorer
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Laplace Variational Inference for Dirichlet Process Mixtures of Marked Poisson Point Processes
A Dirichlet process mixture model for marked Poisson point processes with squared-link intensities and Laplace variational inference jointly infers clusters, cluster count, and continuous mark-specific intensity surfaces.
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Bayesian Modeling and Prediction of Generalized Contact Matrices
A Bayesian model for multi-feature contact matrices that uses tensor structures and contingency table theory to satisfy structural constraints and impute missing contact features, validated on simulations and US/German survey data.
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Principal Nested Cones
Principal Nested Cones is a nonlinear dimension reduction technique that projects cone-structured data onto nested lower-dimensional cones to jointly represent size and shape variation.
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Quadratic Forms in Gaussian Random Variables Theoretical Results and Applications
A review summarizing definitions, canonical forms, exact and approximate distributions, numerical methods, applications, and open problems for quadratic forms in real and complex Gaussian variables, including multiforms and ratios.