Dynamic Discrete Choice Networks Home Page
This is the home page for the NSF Robust Intelligence Project: Dynamic Discrete Choice Networks.
Software
MOCAPY Dynamic Bayesian Networks Python Library This uses Python and Numpy, and supports parallel processing.
[http://www.openbayes.org/ Open Bayes is aian Python library for Bayesian Networks adapted from Kevin Murphy's BNT Toolbox in Matlab. It only supports static BNs, and still uses Numarray.
PyMC is a Markov Chain Monte Carlo library for Python, with many distributions and likelihoods coded in Fortran for speed.
uDraw(Graph) is a Graph editor and visualization package.
Resource Papers
Dynamic Bayesian Networks
Murphy, K. 2002 Dynamic Bayesian Networks: Representation, Inference and Learning - Dissertation
Gharamany - 1997 Learning Dynamic Bayesian Networks
Doucet,Freitas, Murphy, Russell - 2000 Rao-Blackwellised particle filtering for dynamic Bayesian networks
Lerner,UN - 2002 Hybrid Bayesian Networks for Reasoning About Complex Systems - Dissertation
Generalized Extreme Value Models and other Interesting Variants
Bierlaire, M. - 2002 The network GEV model
Wen, Koppelman - 2001 The generalized nested logit model
Ben-Akiva, Mcfadden, Train ... - 2002 Hybrid Choice Models: Progress and Challenges
Train, Weeks - 2004 Discrete Choice Models in Preference Space and Willingness-to Pay Space
Dynamic Discrete Choice Models
Imai, Jain and Ching - 2002 Bayesian Estimation of Dynamic Discrete Choice Models
Heckman and Navarro - 2007 Dynamic Discrete Choice and Dynamic Treatment Effects NBER Working Paper
Bayesian Demand Supply Systems based on Discrete Choice Demand
Yang, Chen, Allenby - 2003 Bayesian Analysis of Simultaneous Demand and Supply
Modeling Dynamic Traffic
Kwon and Murphy - 2000 Modeling Freeway Traffic with Coupled HMMs
Tebaldi, West, Carr - 2001 Statistical analyses of freeway traffic flows
Gogate et al - 2005 Modeling transportation routines using hybrid dynamic mixed networks
Sun, Zhang, Yu - 2006 A Bayesian Network Approach to Traffic Flow Forecasting
