Semantic Scholar Open Access 2020 106 sitasi

Marginal likelihood computation for model selection and hypothesis testing: an extensive review

F. Llorente Luca Martino D. Delgado J. Lopez-Santiago

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Abstrak

This is an up-to-date introduction to, and overview of, marginal likelihood computation for model selection and hypothesis testing. Computing normalizing constants of probability models (or ratio of constants) is a fundamental issue in many applications in statistics, applied mathematics, signal processing and machine learning. This article provides a comprehensive study of the state-of-the-art of the topic. We highlight limitations, benefits, connections and differences among the different techniques. Problems and possible solutions with the use of improper priors are also described. Some of the most relevant methodologies are compared through theoretical comparisons and numerical experiments.

Topik & Kata Kunci

Computer Science Mathematics

Penulis (4)

F. Llorente

Luca Martino

D. Delgado

J. Lopez-Santiago

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APA MLA BibTeX

Llorente, F., Martino, L., Delgado, D., Lopez-Santiago, J. (2020). Marginal likelihood computation for model selection and hypothesis testing: an extensive review. https://doi.org/10.1137/20M1310849

Akses Cepat

Lihat di Sumber doi.org/10.1137/20M1310849

Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 106×
Sumber Database: Semantic Scholar
DOI: 10.1137/20M1310849
Akses: Open Access ✓