Rémy Chapelle · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31524 · open-access PDF
Estimasi nonparametrik distribusi probabilitas sendi adalah masalah inti dalam statistik, yang terkenal menantang dengan data dimensi tinggi. Tantangan seperti itu juga muncul dengan perkiraan distribusi kondisional dan telah menyebabkan algoritma fleksibel yang bertujuan untuk mengatasi "setan dimensionalitas" dengan memanfaatkan struktur prediktif dimensi yang lebih rendah dari ruang lingkungan. Dalam makalah ini, kami mempelajari penggunaan perkiraan distribusi kondisional tersebut untuk memperkirakan distribusi sendi dengan faktorisasi yang terakhir dengan cara "autoregressive".
Machine translation (Meta M2M100); the English original follows.
Nonparametric estimation of joint probability distributions is a core problem in statistics, which is notoriously challenging with high-dimensional data. Such challenges also arise with conditional distribution estimation and have led to flexible algorithms that aim to tackle the ''curse of dimensionality'' by exploiting predictive structures of lower dimension than the ambient space. In this paper, we study the use of such conditional distribution estimators to estimate joint distributions by factorizing the latter in an ''autoregressive'' way. We show that for loss functions satisfying a sub
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