I am developing a framework for optimization in continuous domain and I need to compute the k most probable configurations (most probable explanations) in a Gaussian network. Any suggestion of sample source code or library (R (preferred), Matlab, C++, Python, Java, etc) is welcome.
No, the question is related to Gaussian networks, it is not related to the k nearest neighbours.
Do you refer in fact to Estimation of Distribution Algorithm (EDA) for clustering and learning Gaussian distribution in continuous setting?
Yes, I am referring to estimation of distributions algorithms, more precisely, to use the k most probable configurations as elitism operator. We are also studying other applications of this algorithm in the EDA field.
I suggest that you check out the following reference:
- Sill et al., " c060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models ", 2014 -
Article c060: Extended Inference with Lasso and Elastic-Net Regulari...
Investigating the bibliographic part of the publication by Sill et al. (refer to my prior post) leads to the tgp package for R. Follow:
- R.B. Gramacy, " tgp: An R Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models ", 2007 - https://www.jstatsoft.org/article/view/v019i09/v19i09.pdf
- Taddy et al., " Dynamic Trees for Learning and Design ", 2009 - https://pdfs.semanticscholar.org/facf/7d6a54c64d439b3c739741d2871718ba3ffe.pdf
Additional (intermediairy) references of interest:
- Fröhlich, et al., " Efficient Parameter Selection for Support Vector Machines in Classification and Regression via Model-Based Global Optimization ", 2005 -
Conference Paper Efficient Parameter Selection for Support Vector Machines in...
- Gramacy et al., " Variable Selection and Sensitivity Analysis using Dynamic Trees with an Application to Computer Code Performance Tuning ", 2013 - https://projecteuclid.org/download/pdfview_1/euclid.aoas/1365527190
- Csato et al., " Sparse Online Gaussian Processes ", 2002 - https://pdfs.semanticscholar.org/085e/7e7d093164d6a9408e107e0ab56b6d92563b.pdf
- Shi etal., " Penalized gaussian process regression and classification for high-dimensional nonlinear data ", 2010 - https://eprint.ncl.ac.uk/file_store/production/175017/87C9AAA9-F7F8-4D15-A2F3-26A833B986AF.pdf
Finally, to bind all above pieces of the puzzle, follow jointly:
- Karshenas et al., " Regularized continuous estimation of distribution algorithms ", 2013 - https://pdfs.semanticscholar.org/1688/296dcdcc53a547f06b085be8446c7bd84643.pdf
- Xu et al., " A Fast Elitism Gaussian Estimation of Distribution Algorithm and Application for PID Optimization ", 2014 - https://www.hindawi.com/journals/tswj/2014/597278/
- Pelikan et al., " Introduction to Estimation of Distribution Algorithms ", 2012 - https://pdfs.semanticscholar.org/63c6/c3877530bb91fe94beba6093d1bf1c28f360.pdf
Hoping it will be helpful.
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