Other References¶
Cédric Villani. Optimal Transport. Volume 338 of Grundlehren der mathematischen Wissenschaften. Springer Berlin Heidelberg, Berlin, Heidelberg, 2009. ISBN 978-3-540-71049-3. URL: http://link.springer.com/10.1007/978-3-540-71050-9, doi:10.1007/978-3-540-71050-9.
V I Bogachev, A V Kolesnikov, and K V Medvedev. Triangular transformations of measures. Sbornik: Mathematics, 196(3):309–335, 2005. doi:10.1070/SM2005v196n03ABEH000882.
Herbert Knothe. Contributions to the Theory of Convex Bodies. The Michigan Mathematical Journal, 4(1):39–52, 1957. doi:10.1307/mmj/1028990175.
Murray Rosenblatt. Remarks on a Multivariate Transformation. The Annals of Mathematical Statistics, 23(3):470–472, 1952. doi:10.1214/aoms/1177729394.
Annett B. Sullivan, Dean M. Snyder, and Stewart A. Rounds. Controls on biochemical oxygen demand in the upper Klamath River, Oregon. Chemical Geology, 269(1-2):12–21, 2010. doi:10.1016/j.chemgeo.2009.08.007.
Christian P. Robert and George Casella. Monte Carlo Statistical Methods. Volume 1 of Springer Texts in Statistics. Springer New York, New York, NY, 2004. ISBN 978-1-4419-1939-7. doi:10.1007/978-1-4757-4145-2.
John Hull and Alan White. The Pricing of Options on Assets with Stochastic Volatilities. The Journal of Finance, 42(2):281–300, jun 1987. doi:10.1111/j.1540-6261.1987.tb02568.x.
Sangjoon Kim, Neil Shephard, and Siddhartha Chib. Stochastic volatility: likelihood inference and comparison with ARCH models. The Review of Economic Studies, 65(December 1994):361–393, 1998. doi:10.1111/1467-937X.00050.
Steffen L. Lauritzen. Graphical Models. Graphical Models, 1996.
Robert Grover Brown and Patrick Y. C. Hwang. Introduction to random signals and applied Kalman filtering: with MATLAB exercises and solutions. John Wiley & Sons, Inc., 3rd edition, 1997.
B. E. Bona and Robert J. Smay. Optimum Reset of Ship's Inertial Navigation System. IEEE Transactions on Aerospace and Electronic Systems, AES-2(4):409–414, 1966. doi:10.1109/TAES.1966.4501790.
R E Kalman. A New Approach to Linear Filtering and Prediction Problems 1. Journal of Fluids Engineering, 82(Series D):35–45, 1960. doi:10.1115/1.3662552.
J. Durbin and S. J. Koopman. Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 62(1):3–56, feb 2000. doi:10.1111/1467-9868.00218.
Håvard Rue, Sara Martino, and Nicolas Chopin. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71(2):319–392, apr 2009. doi:10.1111/j.1467-9868.2008.00700.x.