484
Litt´ erature
86. Gunnarsson, F., Nordlund, P.-J., and Gustafsson, F. (2002). Particle
filters for positioning in wireless networks. In Proc. of EUSIPCO, Toulouse.
87. Gustafsson, F., Gunnarsson, F., Bergman, N., Forssell, U., Jansson,
J., Karlsson, R., and Nordlund, P.-J. (2002). Particle filters for positioning,
navigation and tracking. IEEE Transactions on Signal Processing, 50(2).
88. Hara T. and Slade G. (1992), Self-avoiding walks in five or more dimensions :
I. The critical behaviour. Communications in Mathematical Physics, 147, pp.
101-136
89. Haight F.A. Handbook of the Poisson distribution, John Wiley and Sons, New
York (1967).
90. M. P. Hassel. Density-dependence in single-species populations. Journal of
Animal Ecology, vol. 44, pp. 283–295 (1975).
91. M. P. Hassell, J. H. Lawton, and R. M. May. Patterns of dynamical behaviour in single- species populations. Journal of Animal Ecology, vol. 45, pp.
471–486 (1976).
92. A. W. Heemink, M.Verlaan, A. J. Segers. Variance reduced ensemble Kalman filtering. Monthly Weather Review, vol. 129, no. 7, pp. 1718–1728 (2001).
93. K. Itˆ o. Stochastic Integral. Proc. Imperial Acad. Tokyo, vol. 20, pp. 519–524
(1944).
94. K. Itˆ o. On stochastic differential equations. Memoirs, American Mathematical
Society, vol. 4, pp. 1–51 (1951).
95. C. Jarzynski. Equilibrium free energy differences from nonequilibrium measurements : a master equation approach, Phys. Rev. E 56(5), pp. 5018-5035
(1997).
96. Jasra, A. and Singh, S.S. and Martin, J.S. and McCoy, E. Filtering via
approximate Bayesian computation. Statistics and Computing pp. 1573-1375
(2010).
97. M. Kac. Foundations of kinetic theory. Proceedings 3rd Berkeley Sympos. Math.
Stat. and Proba., vol. 3, pp. 171-197 (1956).
98. R. E. Kalman. A new approach to linear filtering and prediction problems.
Trans. ASME Ser. D. J. Basic Engrg. vol. 82, pp. 35–45 (1960).
99. R. E. Kalman, R. S. Bucy. New results in linear filtering and prediction
theory. Trans. ASME Ser. D. J. Basic Engrg.vol. 83, pp. 95–108 (1961).
100. I. Karatzas and S.E. Shreve. Methods of Mathematical Finance. Springer
(1998).
101. I. Karatzas and S. E. Shreve. Brownian Motion and Stochastic Calculus,
Graduate Texts in Mathematics, Springer (2004).
102. S. Karlin, J. McGregor. Coincidence probabilities, Pacific J. Math. :9 11411164 (1959).
103. P. E. Kloeden, E. Platen, and N. Hofmann. Extrapolation methods for
the weak approximation of Itˆ o diffusions. SIAM Journal on Numerical Analysis,
vol. 32, no. 5, pp. 1519–1534 (1995).
104. P. E. Kloeden and E. Platen. Numerical Solution of Stochastic Differential
Equations, vol. 23, Springer (2011).
Litt´ erature
86. Gunnarsson, F., Nordlund, P.-J., and Gustafsson, F. (2002). Particle
filters for positioning in wireless networks. In Proc. of EUSIPCO, Toulouse.
87. Gustafsson, F., Gunnarsson, F., Bergman, N., Forssell, U., Jansson,
J., Karlsson, R., and Nordlund, P.-J. (2002). Particle filters for positioning,
navigation and tracking. IEEE Transactions on Signal Processing, 50(2).
88. Hara T. and Slade G. (1992), Self-avoiding walks in five or more dimensions :
I. The critical behaviour. Communications in Mathematical Physics, 147, pp.
101-136
89. Haight F.A. Handbook of the Poisson distribution, John Wiley and Sons, New
York (1967).
90. M. P. Hassel. Density-dependence in single-species populations. Journal of
Animal Ecology, vol. 44, pp. 283–295 (1975).
91. M. P. Hassell, J. H. Lawton, and R. M. May. Patterns of dynamical behaviour in single- species populations. Journal of Animal Ecology, vol. 45, pp.
471–486 (1976).
92. A. W. Heemink, M.Verlaan, A. J. Segers. Variance reduced ensemble Kalman filtering. Monthly Weather Review, vol. 129, no. 7, pp. 1718–1728 (2001).
93. K. Itˆ o. Stochastic Integral. Proc. Imperial Acad. Tokyo, vol. 20, pp. 519–524
(1944).
94. K. Itˆ o. On stochastic differential equations. Memoirs, American Mathematical
Society, vol. 4, pp. 1–51 (1951).
95. C. Jarzynski. Equilibrium free energy differences from nonequilibrium measurements : a master equation approach, Phys. Rev. E 56(5), pp. 5018-5035
(1997).
96. Jasra, A. and Singh, S.S. and Martin, J.S. and McCoy, E. Filtering via
approximate Bayesian computation. Statistics and Computing pp. 1573-1375
(2010).
97. M. Kac. Foundations of kinetic theory. Proceedings 3rd Berkeley Sympos. Math.
Stat. and Proba., vol. 3, pp. 171-197 (1956).
98. R. E. Kalman. A new approach to linear filtering and prediction problems.
Trans. ASME Ser. D. J. Basic Engrg. vol. 82, pp. 35–45 (1960).
99. R. E. Kalman, R. S. Bucy. New results in linear filtering and prediction
theory. Trans. ASME Ser. D. J. Basic Engrg.vol. 83, pp. 95–108 (1961).
100. I. Karatzas and S.E. Shreve. Methods of Mathematical Finance. Springer
(1998).
101. I. Karatzas and S. E. Shreve. Brownian Motion and Stochastic Calculus,
Graduate Texts in Mathematics, Springer (2004).
102. S. Karlin, J. McGregor. Coincidence probabilities, Pacific J. Math. :9 11411164 (1959).
103. P. E. Kloeden, E. Platen, and N. Hofmann. Extrapolation methods for
the weak approximation of Itˆ o diffusions. SIAM Journal on Numerical Analysis,
vol. 32, no. 5, pp. 1519–1534 (1995).
104. P. E. Kloeden and E. Platen. Numerical Solution of Stochastic Differential
Equations, vol. 23, Springer (2011).