Assessment and Adjustment of Approximate Inference Algorithms Using the Law of Total Variance | PERPUSTAKAAN UNIVERSITAS KATOLIK PARAHYANGAN

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Assessment and Adjustment of Approximate Inference Algorithms Using the Law of Total Variance

Yu, Xuejun - Nama Orang; Nott, David J. - Nama Orang; Tran, Minh-Ngoc - Nama Orang; Klein, Nadja - Nama Orang;

A common method for assessing validity of Bayesian sampling or approximate inference methods makes use of simulated data replicates for parameters drawn from the prior. Under continuity assumptions, quantiles of functions of the simulated parameter values for corresponding posterior distributions are uniformly distributed. Checking for uniformity when a posterior density is approximated numerically provides a diagnostic for algorithm validity. Furthermore, adjustments to achieve uniformity can improve the quality of approximate inference methods. The present article develops a moment-based alternative to the conventional checking and adjustment methods using quantiles. The new approach relates prior and posterior expectations and covariances through the tower property of conditional expectation and the law of total variance. For adjustment, approximate inferences are modified so that the correct prior to posterior relationships hold. We illustrate the method in three examples. The first uses an auxiliary model in a likelihood-free inference problem. The second considers corrections for variational Bayes approximations in a deep neural network generalized linear mixed model. Our final application considers a deep neural network surrogate for approximating Gaussian process regression predictive inference. Supplementary files for this article are available online.

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Barcode		Tipe Koleksi	Nomor Panggil	Lokasi	Status
art140484	null	Artikel		Gdg9-Lt3	Tersedia namun tidak untuk dipinjamkan - No Loan

Informasi Detail

Judul Seri: JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS; Vol.30 No.4 December 2021
No. Panggil: -
Penerbit: : .,
Deskripsi Fisik: p. 977-990
Bahasa: English
ISBN/ISSN: -
Klasifikasi: NONE
Tipe Isi: -
Tipe Media: -
Tipe Pembawa: -
Edisi: -
Subjek: MARKOV CHAIN MONTE CARLO
VARIATIONAL APPROXIMATION
GAUSSIAN PROCESS
DEEP NEURAL NETWORK
LIKELIHOOD-FREE INFERENCE
LAW OF TOTAL VARIANCE
Info Detail Spesifik: https://doi.org/10.1080/10618600.2021.1880921
Pernyataan Tanggungjawab: Xuejun Yu, David J. Nott, Minh-Ngoc Tranz, Nadja Klein

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