MARKOV CHAIN MONTE CARLO INFERENCE FOR PROBABILISTIC LATENT TENSOR FACTORIZATION

被引：0

作者：

Simsekli, Umut ^{[1
]}

Cemgil, A. Taylan ^{[1
]}

机构：

[1] Bogazici Univ, Dept Comp Engn, TR-34342 Istanbul, Turkey

来源：

2012 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP) | 2012年

关键词：

Probabilistic Latent Tensor Factorization (PLTF); Markov Chain Monte Carlo (MCMC); Space Alternating Data Augmentation (SADA);

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Probabilistic Latent Tensor Factorization (PLTF) is a recently proposed probabilistic framework for modeling multiway data. Not only the popular tensor factorization models but also any arbitrary tensor factorization structure can be realized by the PLTF framework. This paper presents Markov Chain Monte Carlo procedures (namely the Gibbs sampler) for making inference on the PLTF framework. We provide the abstract algorithms that are derived for the general case and the overall procedure is illustrated on both synthetic and real data.

引用

页数：6

共 50 条

[31] ANNEALING MARKOV-CHAIN MONTE-CARLO WITH APPLICATIONS TO ANCESTRAL INFERENCE
GEYER, CJ
THOMPSON, EA
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1995, 90 (431) : 909 - 920
[32] Parallel metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference
Altekar, G
Dwarkadas, S
Huelsenbeck, JP
Ronquist, F
BIOINFORMATICS, 2004, 20 (03) : 407 - 415
[33] Phylogenetic inference for binary data on dendograms using Markov chain Monte Carlo
Mau, B
Newton, MA
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 1997, 6 (01) : 122 - 131
[34] Markov Chain Monte Carlo Used in Parameter Inference of Magnetic Resonance Spectra
Hock, Kiel
Earle, Keith
ENTROPY, 2016, 18 (02):
[35] Markov Chain Monte Carlo on Matrix Manifolds for Probabilistic Model Order Reduction
Vizzaccaro, Alessandra
Lykkegaard, Mikkel B.
Dodwell, Tim
DATA SCIENCE IN ENGINEERING, VOL. 10, IMAC 2024, 2025, : 93 - 95
[36] PROBABILISTIC LATENT TENSOR FACTORIZATION FRAMEWORK FOR AUDIO MODELING
Cemgil, Ali Taylan
Simsekli, Umut
Subakan, Yusuf Cem
2011 IEEE WORKSHOP ON APPLICATIONS OF SIGNAL PROCESSING TO AUDIO AND ACOUSTICS (WASPAA), 2011, : 137 - 140
[37] Application of the transitional Markov chain Monte Carlo algorithm to probabilistic site characterization
Ching, Jianye
Wang, Jiun-Shiang
ENGINEERING GEOLOGY, 2016, 203 : 151 - 167
[38] AcMC2: Accelerated Markov Chain Monte Carlo for Probabilistic Models
Banerjee, Subho S.
Kalbarczyk, Zbigniew T.
Iyer, Ravishankar K.
TWENTY-FOURTH INTERNATIONAL CONFERENCE ON ARCHITECTURAL SUPPORT FOR PROGRAMMING LANGUAGES AND OPERATING SYSTEMS (ASPLOS XXIV), 2019, : 515 - 528
[39] Use of Rifampin Compared with Isoniazid for the Treatment of Latent Tuberculosis Infection in Japan: A Bayesian Inference with Markov Chain Monte Carlo Method
Iwata, Kentaro
Morishita, Naomi
Nishiwaki, Masami
Miyakoshi, Chisato
INTERNAL MEDICINE, 2020, 59 (21) : 2687 - 2691
[40] Probabilistic estimation of variogram parameters of geotechnical properties with a trend based on Bayesian inference using Markov chain Monte Carlo simulation
Xu, Jiabao
Zhang, Lulu
Li, Jinhui
Cao, Zijun
Yang, Haoqing
Chen, Xiangyu
GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS, 2021, 15 (02) : 83 - 97

← 1 2 3 4 5 →