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Adapting TabPFN for Zero-Inflated Metagenomic Data

This paper introduces a novel prior assumption for TabPFN—a meta-learning method designed to approximate Bayesian inference on synthetic datasets generated from a predefined prior—aimed at better accommodating the unique zero-inflated distributions characteristic of metagenomic data.
Perciballi, Giulia   +5 more
openaire   +1 more source

Variable selection approach for zero-inflated count data via adaptive lasso

Journal of Applied Statistics, 2014
Ping Zeng, Yongyue Wei, Yang Zhao
exaly  

Statistical inference for zero-and-one-inflated poisson models

Statistical Theory and Related Fields, 2017
Yincai Tang, Ancha Xu
exaly  

Principal component analysis for zero-inflated compositional data

Computational Statistics & Data Analysis
Kipoong Kim, Jaesung Park, Sungkyu Jung
openaire   +1 more source

Decision tree approaches for zero-inflated count data

Journal of Applied Statistics, 2006
exaly  

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