Optimal model inference for Bayesian mixture of experts

被引：0
|
作者：
Ueda, N [1 ]
Ghahramani, Z [1 ]
机构：
[1] NTT, Commun Sci Labs, Kyoto 6190237, Japan
来源：
NEURAL NETWORKS FOR SIGNAL PROCESSING X, VOLS 1 AND 2, PROCEEDINGS | 2000年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We present an algorithm for inferring the parameter and model structure of a mixture of experts model (MoE) based on the variational Bayesian (VB) framework. First, in the VB framework, we show that the model parameter and structure of a MoE can be simultaneously optimized by maximizing an objective funtion derived in this paper. Next, we present a deterministic algorithm to find the optimal number of experts of a MoE while avoiding local maxima. Our experimental results demonstrate the practical usefulness of the method.
引用
收藏
页码：145 / 154
页数：10
相关论文
共 50 条
  • [41] Markov chain Monte Carlo simulation of a Bayesian mixture model for gene network inference
    Ko, Younhee
    Kim, Jaebum
    Rodriguez-Zas, Sandra L.
    GENES & GENOMICS, 2019, 41 (05) : 547 - 555
  • [42] Optimal inference with suboptimal models: Addiction and active Bayesian inference
    Schwartenbeck, Philipp
    FitzGerald, Thomas H. B.
    Mathys, Christoph
    Dolan, Ray
    Wurst, Friedrich
    Kronbichler, Martin
    Friston, Karl
    MEDICAL HYPOTHESES, 2015, 84 (02) : 109 - 117
  • [43] Inference for the Number of Topics in the Latent Dirichlet Allocation Model via Bayesian Mixture Modeling
    Chen, Zhe
    Doss, Hani
    JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2019, 28 (03) : 567 - 585
  • [44] RETRACTED: Bayesian Inference of Mixture Model via Differential Evolution and Sampling (Retracted Article)
    Guo, Peng
    Li, Naixiang
    2ND IEEE INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER CONTROL (ICACC 2010), VOL. 4, 2010, : 504 - 508
  • [45] Bayesian Inference for Finite Mixture Regression Model Based on Non-Iterative Algorithm
    Shan, Ang
    Yang, Fengkai
    MATHEMATICS, 2021, 9 (06)
  • [46] Mixture of experts classification using a hierarchical mixture model
    Titsias, MK
    Likas, A
    NEURAL COMPUTATION, 2002, 14 (09) : 2221 - 2244
  • [47] Nonlinear Time Series Analysis Using Bayesian Mixture of Experts
    Baldacchino, Tara
    Rowson, Jennifer
    Worden, Keith
    NONLINEAR DYNAMICS, VOL 2, 2014, : 111 - 121
  • [48] Inference of gene pathways using mixture Bayesian networks
    Ko, Younhee
    Zhai, ChengXiang
    Rodriguez-Zas, Sandra
    BMC SYSTEMS BIOLOGY, 2009, 3
  • [49] Bayesian inference for some mixture problems in quality and reliability
    Nair, VN
    Tang, B
    Yu, LA
    JOURNAL OF QUALITY TECHNOLOGY, 2001, 33 (01) : 16 - 28
  • [50] Bayesian Inference for the Loss Models via Mixture Priors
    Deng, Min
    Aminzadeh, Mostafa S.
    RISKS, 2023, 11 (09)
12345