References and Further Reading


Prof. Dr. Claus Möbus

Room: A02 2-226

Tel: +49 441 / 798-2900 



Manuela Wüstefeld

Room: A02 2-228

Tel: +49 441 / 798-4520 


References and Further Reading

References and Further Reading

Adams, N. & Rees, J., Object-Oriented Programming in Scheme, AI-Memo, MIT, AI-Lab, 1989; (visited 2016/04/02)

Albert, J., Bayesian Computation with R, Berlin-Heidelberg: Springer, 2009, 2/e, ISBN 978-0-387-92297-3

Barber, David. Bayesian Reasoning and Machine Learning, 2012, Cambridge University Press, ISBN: 978-0-521-51814-7

Barber, David. Bayesian Reasoning and Machine Learning, 2016; (visited 2019/01/19)

Bishop, Christopher M., and Julia Lasserre. Generative or discriminative? getting the best of both worlds. Bayesian Statistics 8 (2007): 3-24.

Flury, B.D. Acceptance-rejection sampling made easy. SIAM Review 32.3, 1990, 474-476    

Howard, R.A., Dynamic Probabilistic Systems, Vol. I: Markov Models, New York: John Wiley & Sons, 1971, ISBN 0-471-41665-7

Kelly, John. The Essence of Logic, Prentice Hall, 1996, ISBN-13: 978-0133963755

Kelly, John. Logik im Klartext, Pearson Education, 2003, ISBN: 3-8273-7070-1

Klenke, A., Probability Theory, Berlin-Heidelberg: Springer, 2014, 2/e, 2014, ISBN 978-1-4471-5360-3

Lynch, Scott M., Introduction to Applied Bayesian Statistics and Estimation for Social Scientists, Berlin-Heidelberg: Springer, 2007, ISBN 978-0-387-712642

MacKay, D.J.C., Information Theory, Inference, and Learning Algorithms, Cambridge, UK: Cambridge University Press, 2003, ISBN 0521642981

Marin, J-M. & Robert, Chr.P., Bayesian Essentials with R, Berlin-Heidelberg: Springer, 2/e, 2014, ISBN 978-1-4614-8686-2

Morandat, Floréal, et al., Evaluating the design of the R language, in: ECOOP 2012–Object-oriented programming. Springer Berlin Heidelberg, 2012. S. 104-131.

Mueser, Peter R., and Donald Granberg. The Monty Hall dilemma revisited: Understanding the interaction of problem definition and decision making, Experimental. June (1999). (; visited 2016/07/20)

Murphy, K.P., Machine Learning: A Probabilistic Perspective, Cambridge, Mass.: MIT Press, 2012, ISBN 978-0-262-01802-9

Pearl, Judea; Glymour, Madelyn, and Jewell, Nicolas P.  Causal Inference In Statistics, 2016, Wiley, ISBN: 9781119186847

Pearce, J., Programming and Meta-Programming in Scheme, Berlin-Heidelberg: Springer, 1998, ISBN 0-387-98320-1

Pishro-Nik, Hossein. Introduction to Probability, Statistics, and Random Processes, Kappa Research, LLC, 2014, ISBN-13: 978-0990637202

Pishro-Nik, Hossein. Introduction to Probability, Statistics, and Random Processes  (visited 2019/01/20)

Robert, Chr.P. & Casella, G., Introducing Monte Carlo Methods with R, Berlin-Heidelberg: Springer, 2010, ISBN 978-1-4419-1575-7, DOI 10.1007/978-1-4419-1576-4

Robert, Chr.P. & Casella, G., Monte Carlo Statistical Methods, Berlin-Heidelberg: Springer, 2004, 2/e, ISBN 0-387-21239-6

Russell, St. & Norvig, P., Artificial Intelligence: A Modern Approach, 3/e, 2010, Upper Saddle River, N.J.: Pearson, ISBN 978-0-13-604259-4

Smith & Gelfand, Bayesian Statistics Without Tears: A Sampling-Resampling Perspective, The American Statistician, 1992, Vol.46, No.2, 84-88;

Thrun, S., Burgard, W. & Fox, D., Probabilistic Robotics, Cambridge, MA.: MIT Press, 2005, ISBN 0-262-20162-3

von Neumann, John, Various Techniques Used in Connection with Random Digits, National Bureau of Standards Applied Math Series, 1951, 12: 36-38.

(Changed: 2021-02-03)