486
Litt´ erature
123. G. Marsaglia, A. Zaman. Towards a universal random number generator.
Stat. Prob. Lett., 8, pp. 35-39 (1990).
124. B. K. Øksendal and A. Sulem. Applied Stochastic Control of Jump Diffusions. Springer, Berlin (2007).
125. B. K. Øksendal. Stochastic Differential Equations : An Introduction with
Applications, 6th ed. Springer, Berlin (2003).
126. R. Pemantle. A survey of random processes with reinforcement. Probability
Surveys Vol. 4 (2007) 1 ?79
127. E. Pardoux, D. Talay. Discretization and simulation of stochastic differential
equations. Acta Applic. Math., vol. 3, no. 1, pp. 22–47 (1985).
128. M.K. Pitt, N. Shephard. Filtering via simulation : auxiliary particle filter.
J. Am. Statist. Ass., vol. 94, no. 446, pp 590-599 (1999).
129. M. Planck. Sitz.ber. Preuss. Akad. (1917).
130. O. Radulescu, A. Muller, and A. Crudu. Th´ eor` emes limites pour des
processus de Markov `
a sauts. Synth` ese des r´ esultats et applications en biologie
mol´ eculaire, Technique et Science Informatiques 26, no. 3-4, pp. 443–469 (2007).
131. D. Revuz, M. Yor. Continuous martingales and Brownian motion. Vol. 293.
Springer (1999).
132. W.E. Ricker. Handbook of computation for biological statistics of fish populations. Bulletin 119 of the Fisheries Resource Board, Canada, Ottawa (1958).
133. C.P. Robert and G. Casella. Monte Carlo Statistical Methods (second
edition). New York : Springer-Verlag (2004).
134. H. Robbins, S. Monro. A Stochastic Approximation Method. The Annals of
Mathematical Statistics, vol. 22, no. 3, pp. 400–407 (1951).
135. M. Rousset & G. Stoltz. Equilibrium sampling from non-equilibrium dynamics. J. Stat. Phys., 123, 1251-1272 (2006).
136. R. Y. Rubinstein and D. P. Kroese. Simulation and the Monte Carlo Method (second edition). New York : John Wiley & Sons (2007).
137. B. Sapoval. Universalit´ es et fractales. Jeux d’enfants ou d´ elits d’initi´ e. Flammarion Paris (1997).
138. T. Sellke. On the asymptotic distribution of the size of a stochastic epidemic.
J. Appl. Prob., vol. 20, no. 390 (1983).
139. Shiga, T. & Tanaka, H. (1985). Central limit theorem for a system of Markovian particles with mean field interaction, Zeitschrift f¨ ur Wahrscheinlichkeitstheorie verwandte Gebiete, 69, 439–459.
140. Singer, R.A. Estimating Optimal Tracking Filter Performance for Manned
Maneuvering Targets. IEEE Transactions on Aerospace and Electronic Systems,
AES-6(4) :473-483, jul. 1970.
141. J. A. Snyman. Practical Mathematical Optimization : An Introduction to
Basic Optimization Theory and Classical and New Gradient-Based Algorithms.
Springer Publishing (2005).
142. I. Stewart. Dieu joue-t-il aux d` es, les math´ ematiques du chaos, Champs Flammarion (1992).
Litt´ erature
123. G. Marsaglia, A. Zaman. Towards a universal random number generator.
Stat. Prob. Lett., 8, pp. 35-39 (1990).
124. B. K. Øksendal and A. Sulem. Applied Stochastic Control of Jump Diffusions. Springer, Berlin (2007).
125. B. K. Øksendal. Stochastic Differential Equations : An Introduction with
Applications, 6th ed. Springer, Berlin (2003).
126. R. Pemantle. A survey of random processes with reinforcement. Probability
Surveys Vol. 4 (2007) 1 ?79
127. E. Pardoux, D. Talay. Discretization and simulation of stochastic differential
equations. Acta Applic. Math., vol. 3, no. 1, pp. 22–47 (1985).
128. M.K. Pitt, N. Shephard. Filtering via simulation : auxiliary particle filter.
J. Am. Statist. Ass., vol. 94, no. 446, pp 590-599 (1999).
129. M. Planck. Sitz.ber. Preuss. Akad. (1917).
130. O. Radulescu, A. Muller, and A. Crudu. Th´ eor` emes limites pour des
processus de Markov `
a sauts. Synth` ese des r´ esultats et applications en biologie
mol´ eculaire, Technique et Science Informatiques 26, no. 3-4, pp. 443–469 (2007).
131. D. Revuz, M. Yor. Continuous martingales and Brownian motion. Vol. 293.
Springer (1999).
132. W.E. Ricker. Handbook of computation for biological statistics of fish populations. Bulletin 119 of the Fisheries Resource Board, Canada, Ottawa (1958).
133. C.P. Robert and G. Casella. Monte Carlo Statistical Methods (second
edition). New York : Springer-Verlag (2004).
134. H. Robbins, S. Monro. A Stochastic Approximation Method. The Annals of
Mathematical Statistics, vol. 22, no. 3, pp. 400–407 (1951).
135. M. Rousset & G. Stoltz. Equilibrium sampling from non-equilibrium dynamics. J. Stat. Phys., 123, 1251-1272 (2006).
136. R. Y. Rubinstein and D. P. Kroese. Simulation and the Monte Carlo Method (second edition). New York : John Wiley & Sons (2007).
137. B. Sapoval. Universalit´ es et fractales. Jeux d’enfants ou d´ elits d’initi´ e. Flammarion Paris (1997).
138. T. Sellke. On the asymptotic distribution of the size of a stochastic epidemic.
J. Appl. Prob., vol. 20, no. 390 (1983).
139. Shiga, T. & Tanaka, H. (1985). Central limit theorem for a system of Markovian particles with mean field interaction, Zeitschrift f¨ ur Wahrscheinlichkeitstheorie verwandte Gebiete, 69, 439–459.
140. Singer, R.A. Estimating Optimal Tracking Filter Performance for Manned
Maneuvering Targets. IEEE Transactions on Aerospace and Electronic Systems,
AES-6(4) :473-483, jul. 1970.
141. J. A. Snyman. Practical Mathematical Optimization : An Introduction to
Basic Optimization Theory and Classical and New Gradient-Based Algorithms.
Springer Publishing (2005).
142. I. Stewart. Dieu joue-t-il aux d` es, les math´ ematiques du chaos, Champs Flammarion (1992).