A Bayesian approach to network modularity Jake Hofman Wiggins Lab Columbia University

First Page		Document Content
Date: 2009-01-22 17:20:54 Statistical theory Generative model Machine learning Bayesian inference Bayesian network Biological network inference Statistics Bayesian statistics Statistical models		A Bayesian approach to network modularity Jake Hofman Wiggins Lab Columbia University Add to Reading List Source URL: www.jakehofman.com Download Document from Source Website File Size: 1,81 MB Share Document on Facebook

	Visualizing statistical models: Removing the blindfold Hadley Wickham, Dianne Cook and Heike Hofmann Department of Statistics MSMain St Houston TXe-mail: DocID: 1xVjw - View Document
	Noise-contrastive estimation: A new estimation principle for unnormalized statistical models Michael Gutmann Dept of Computer Science and HIIT, University of Helsinki DocID: 1xUlB - View Document
	Code Completion with Statistical Language Models Veselin Raychev Martin Vechev Eran Yahav DocID: 1xToM - View Document
	An Introduction to the Statistical Analysis of Agent-Based Models Giorgio Fagiolo https://mail.sssup.it/~fagiolo DocID: 1vhKi - View Document
	FallSTA4513: Statistical Models of Networks Lecture 3 — 24 September, 2014 Prof. Daniel M. Roy DocID: 1vfwF - View Document