K. Murphy, Learning Bayes net structure from sparse data sets, 2001.

M. Kevin, Dynamic Bayesian Networks: Representation, Inference and Learning, 2002.

J. C. Owen, CONSTRAINTS ON SIMPLE GEOMETRY IN TWO AND THREE DIMENSIONS, International Journal of Computational Geometry & Applications, vol.06, issue.04, pp.421-434, 1993.
DOI : 10.1142/S0218195996000265

J. Pearl, Probabilistic reasoning in intelligent systems : Networks of plausible inference, 1988.

J. Pineau and S. Thrun, An Integrated Approach to Hierarchy and Abstraction for POMDP, 2002.

W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, Numerical Recipes in FORTRAN: The Art of Scientific Computing, 1992.

L. R. Rabiner and B. H. Juang, An introduction to hidden Markov models, IEEE ASSP Magazine, vol.3, issue.1, pp.4-16, 1986.
DOI : 10.1109/MASSP.1986.1165342

L. R. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proc. of the IEEE Trans. on ASSP, vol.77, issue.2, pp.257-285, 1989.

C. Robert, An entropy concentration theorem: applications in artificial intelligence and descriptive statistics, Journal of Applied Probabilities, 1990.

J. A. Robinson, A Machine Oriented Logic Based on the Resolution Principle, Jour. Assoc. Comput. Mach, vol.12, 1965.

J. A. Robinson, Logic : Form and Function ; North-Holland, 1979.

J. A. Robinson and E. E. Sibert, LOGLISP : an alternative to PROLOG, Machine Intelligence, vol.10, 1983.

J. A. Robinson and E. E. Sibert, LOGLISP : Motivation, design and implementation, Machine Intelligence, vol.10, 1983.

R. Y. Rubinstein, C. Del-rey, C. R. Smith, W. T. Grandy, . F. Jr et al., Simulation and the Monte Carlo method Assemblability based on maximum likelihood configuration of tolerances Maximum-Entropy and bayesian methods in inverse problems Bayesian computation via the Gibbs sampler and related Monte Carlo methods, Proc. of the IEEE Symposium on Assembly and Task Planning, pp.3-23, 1981.

R. H. Taylor, A synthesis of manipulator control programs from task-level specifications, 1976.

S. Thrun, W. Burgard, and D. Fox, A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots, Machine Learning and Autonomous Robots, vol.31, issue.5, pp.1-25, 1998.

S. Thrun, Probabilistic algorithms in robotics, pp.93-109, 2000.

G. Welch and G. Bishop, An introduction to the, 1997.

J. S. Yedidia, W. T. Freeman, Y. Weiss, . Table, . Of et al., Constructing Free-Energy Approximations and Generalized Belief Propagation Algorithms, IEEE Transactions on Information Theory, vol.51, issue.7, pp.2002-2037, 2002.
DOI : 10.1109/TIT.2005.850085
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.118.9766

R. .. Bayesian-estimation, Hidden Markov Models, Kalman Filters and Particle Filters, p.12

B. Learning, A. , P. Baum-welch-algorithm, and .. , 35 How to identify (learn) the value of the free parameters?35 How to compare different probabilistic models (specifications)?36 How to find interesting decompositions and associated parametric forms?37 How to find the pertinent variables to model a phenomenon?, .39 LEARNING STRUCTURE OF, pp.37-40