]. P. Bibliographie1 and . Cousinou, Le risque de criticité et sa prévention dans les usines, les laboratoires et les transports, 2001.

J. Gomit, Cristal v1 : Criticality package for burnup credit calculation, Proc. of the NCSD 2005 Meeting on Integrating Criticality Safety into the Resurgence of Nuclear Power, 2005.

P. Brémaud, Introduction aux probabilités, 1984.

G. Comte-de-buffon, Essai d'arithmétique morale, 1777.

N. Metropolis, The beginning of the Monte Carlo method, Los Alamos Science, Special issue, 1987.

G. E. Whitesides, A difficulty in computing the k-effective of the world, Trans. Am. Nucl. Soc, 1971.

N. Report, Source Convergence in Criticality Safety Analyses. NEA n?5431, 2006.

Y. Richet, Suppression du Régime Transitoire Initial des Simulations Monte Carlo de Criticité, 2006.

N. Martin, Méthode de Monte Carlo avec tables de probabilité pour le calcul neutronique, 2011.

A. Hebert, Applied Reactor Physics, Presses Internationales Polytechniques, 2009.

D. C. Irving, The adjoint Boltzmann equation and its simulation by Monte Carlo, Nuclear Engineering and Design, vol.15, pp.273-292, 1971.
DOI : 10.1016/0029-5493(71)90069-0

. Coveyou, Adjoint and importance in Monte Carlo application. Nuclear science and Engineering, p.219, 1967.

G. Bell, Nuclear Reactor Theory, 1970.

H. Hurwitz, Naval Reactor physics Handbook, 1964.

P. B. Wilson, F. Kiedrowski, and . Brown, Calculating kinetics parameters and reactivity changes with continuous-energy Monte Carlo Advances in Reactor Physics to Power the Nuclear Renaissance, 2010.

A. A. Blyskavka and K. F. Raskach, An experience of applying iterated fission probability method to calculation of effective kinetics parameters and keff sensitivities with Monte Carlo, Advances in Reactor Physics to Power the Nuclear Renaissance, 2010.

B. C. Kiedrowski, Adjoint Weighting for continuous-energy Monte Carlo radiation Transport, 2009.

J. Wagner, Acceleration of Monte Carlo shielding calculations with an automated variance reduction technique and parallel processing, 1997.

O. Zamonsky, Z. Karriem, and K. Ivanov, Development of a consistent monte carlo-deterministic transport methodology based on the method of characteristics and MCNP5, International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, 2011.

R. and L. Tellier, Développement de la méthode des caractéristiques pour le calcul de réseau, 2006.

D. Rozon, Introduction à la cinétique des réacteurs nucléaires, École Polytechnique de Montréal, 1992.

I. Lux and L. Koblinger, Monte Carlo particle transport methods : neutron and photon calculations, 1991.

A. Bielajew, Fundamentals of Monte Carlo method for neutral and charged particle transport. The University of Michigan, 2001.

S. Christoforou, A zero-variance based scheme for Monte Carlo criticality simulations, 2010.

J. E. Hoogenboom, Zero variance monte carlo schemes revisited. Nuclear science and Engineering, pp.1-22, 2008.
DOI : 10.13182/nse160-01

T. E. Booth, Genesis of the weight window and the weight window generator in MCNP -a personal history. LA-UR-06-5807, 2006.

T. E. Booth, A sample problem for variance reduction in mcnp. LA- 10363-MS, 1985.

T. Becker, Hybrid Monte Carlo/Deterministic Methods for Radiation Shielding Problems, 2009.

A. Haghighat, Monte Carlo variance reduction with deterministic importance functions, Progress in Nuclear Energy, vol.42, issue.1, pp.25-53, 2003.
DOI : 10.1016/S0149-1970(02)00002-1

S. W. Mosher, J. C. Wagner, D. E. Peplow, and T. M. Evans, Review of hybrid (deterministic/monte carlo) radiation transport methods, codes, and applications at oak ridge national laboratory, Progress in nuclear science and technology, pp.808-814, 2011.

S. W. Mosher, T. M. Evans, J. A. Turner, J. C. Wagner, and D. E. Peplow, Hybrid and parallel domain-decomposition methods development to enable monte carlo for reactor analyses, Progress in nuclear science and technology, pp.815-820, 2011.

J. E. Hoogenboom and S. Christoforou, A zero-variance-based scheme for monte carlo criticality calculations. Nuclear science and Engineering, pp.91-104, 2011.

E. Dumonteil and T. Courau, Dominance ratio assessment and Monte Carlo criticality simulations : dealing with high dominance ratio systems, Nuclear Technology, vol.172, 2010.

T. Ueki and F. Brown, Stationarity diagnostics using Shannon entropy in monte carlo criticality calculation I : F test, 2002.

F. Brown, On the use of Shannon entropy of the fission distribution for assessing convergence of monte carlo criticality calculations, ANS Topical Meeting on Reactor Physics, 2006.

F. Brown and B. C. Kiedrowski, Difficulties computing k in non-uniform, multi-region systems with loose, asymmetric coupling, ICNC, 2011.

S. S. Wilks, Determination of sample size for setting tolerance limits. The Annals of Mathematical statistics, pp.91-96, 1941.

L. Heulers, MORET5 overview of the new capabilities implemented in the multigroup/continuous-energy version, ICNC, 2011.

R. J. Brissenden and A. R. Garlick, Biases in the estimation of Keff and its error by Monte Carlo methods, Annals of Nuclear Energy, vol.13, issue.2, pp.63-83, 1986.
DOI : 10.1016/0306-4549(86)90095-2

R. N. Blomquist and E. M. Gelbard, Alternative Implementations of the Monte Carlo Power Method, Nuclear Science and Engineering, vol.141, issue.2, pp.85-100, 2002.
DOI : 10.13182/NSE01-30

T. Yamamoto and Y. Miyoshi, Reliable Method for Fission Source Convergence of Monte Carlo Criticality Calculation with Wielandt's Method, Journal of Nuclear Science and Technology, vol.39, issue.2, pp.99-107, 2004.
DOI : 10.1080/18811248.2004.9715465

R. , R. G. Marleau, and A. Hebert, A User Guide for DRAGON Version 4. Rapport technique IGE-294, 2004.

O. Jacquet, Eigenvalue uncertainty evaluation in MC calculations , using time series methodologies Advanced Monte Carlo for Radiation Physics, Particle Transport Simulation and Applications, 2001.

Y. Richet, Initialization bias suppression of an iterative monte carlo calculation. The Monte Carlo Method : Versatility unbounded in a dynamic computing world, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00409762

A. Jinaphanh, Automated convergence detection in Monte Carlo criticality calculations using student's bridge statistics based on Keff or Shannon entropy, ANS Physics of Reactors Topical Meeting, 2012.

J. Densmore, Variational Variance Reduction for Monte Carlo reactor analysis, 2002.

A. Jinaphanh, Exploring the use of a deterministic adjoint flux calculation in criticality monte carlo simulations, International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, 2011.

L. Encadrement-de, entropie de Shannon pour une initialisation uniforme et une initialisation par un adjoint : zoom sur les premières étapes, p.121

*. Mac, MACRO MACROTMP :: MIX 1 1 UPDL MIX 2 1 OLDL

*. M2t, MACRO :: PN 0 * MIX FUEL1 FROM 1 ENDMIX * MIX WATER FROM 2 ENDMIX