Calculating Principal Eigen-Functions of Non-Negative Integral Kernels: Particle Approximations and Applications

Published Online:https://doi.org/10.1287/moor.2016.0834

References

  • Albertini F, Runggaldier WJ (1988) Logarithmic transformations for discrete-time, finite-horizon stochastic control problems. Appl. Math. Optim. 18(1):143–161.CrossrefGoogle Scholar
  • Assaraf R, Caffarel M, Khelif A (2000) Diffusion Monte Carlo methods with a fixed number of walkers. Physical Rev. E 61(4):4566.CrossrefGoogle Scholar
  • Athreya KB (2000) Change of measures for Markov chains and the LlogL theorem for branching processes. Bernoulli 6(2):323–338.CrossrefGoogle Scholar
  • Bierkens J, Kappen B (2011) Online solution of the average cost Kullback-Leibler optimization problem. 4th Internat. Workshop Optim. Machine Learn., OPT ’11. (MIT Press, Cambridge, MA), 1–6.Google Scholar
  • Bucklew JA, Ney P, Sadowsky JS (1990) Monte Carlo simulation and large deviations theory for uniformly recurrent Markov chains. J. Appl. Probab. 20(1):44–59.CrossrefGoogle Scholar
  • Burdzy K, Hołyst R, March P (2000) A Fleming-Viot particle representation of the Dirichlet Laplacian. Commu. Math. Phys. 214(3):679–703.CrossrefGoogle Scholar
  • Chan HP, Lai T (2011) A sequential Monte Carlo approach to computing tail probabilities in stochastic models. Ann. Appl. Probab. 21(6):2315–2342.CrossrefGoogle Scholar
  • Collet P, Martínez S, San Martín J (2012) Quasi-Stationary Distributions: Markov Chains, Diffusions and Dynamical Systems (Springer, Berlin).Google Scholar
  • Cox JC, Ingersoll JE Jr, Ross SA (1985) A theory of the term structure of interest rates. Econometrica 7(2):385–407.CrossrefGoogle Scholar
  • Dai Pra P, Meneghini L, Runggaldier WJ (1996) Connections between stochastic control and dynamic games. Math. Control, Signals Systems 9(4):303–326.CrossrefGoogle Scholar
  • Del Moral P (2004) Feynman-Kac Formulae. Genealogical and Interacting Particle Systems with Applications. Probability and its Applications (Springer, New York).CrossrefGoogle Scholar
  • Del Moral P (2013) Mean Field Simulation for Monte Carlo Integration (CRC Press, Boca Raton, FL).CrossrefGoogle Scholar
  • Del Moral P, Doucet A (2004) Particle motions in absorbing medium with hard and soft obstacles. Stoch. Anal. Appl. 22(5):1175–1207.CrossrefGoogle Scholar
  • Del Moral P, Miclo L (2003) Particle approximations of Lyapunov exponents connected to Schrödinger operators and Feynman Kac semigroups. ESAIM Probab. Stat. 7:171–208.CrossrefGoogle Scholar
  • Del Moral P, Doucet A, Singh SS (2010) A backward particle interpretation of Feynman-Kac formulae. ESAIM Math. Model. Numer. Anal. 44(05):947–975.CrossrefGoogle Scholar
  • Del Moral P, Hu P, Oudjane N (2012) Snell envelope with small probability criteria. Appl. Math. Optim. 66(3):309–330.CrossrefGoogle Scholar
  • Del Moral P, Hu P, Oudjane N, Rémillard B (2011) On the robustness of the Snell envelope. SIAM J. Financial Math. 2(1):587–626.CrossrefGoogle Scholar
  • Douc R, Fort G, Moulines E, Priouret P (2009) Forgetting the initial distribution for hidden Markov models. Stochastic Process. Appl. 119(4):1235–1256.CrossrefGoogle Scholar
  • Douc R, Garivier A, Moulines E, Olsson J (2011) Sequential Monte Carlo smoothing for general state space hidden Markov models. Ann. Appl. Probab. 21(6):2109–2145.CrossrefGoogle Scholar
  • Dupuis P, Ellis RS (2011) A Weak Convergence Approach to the Theory of Large Deviations, Vol. 902 (John Wiley & Sons, New York).Google Scholar
  • Dupuis P, Wang H (2005) Dynamic importance sampling for uniformly recurrent Markov chains. Ann. Appl. Probab. 15(1A):1–38.CrossrefGoogle Scholar
  • Dvijotham K, Todorov E (2011) A unified theory of linearly solvable optimal control. Cozman FG, Pfeffer A, eds. Proc. 27th Conf. Uncertainty in Artificial Intelligence, UAI ’11 (AUAI Press, Corvallis, OR), 179–186.Google Scholar
  • Fleming W (1982) Logarithmic transformations and stochastic control. Advances in Filtering and Optimal Stochastic Control (Springer, Berlin), 131–141.CrossrefGoogle Scholar
  • Fleming WH, Mitter SK (1982) Optimal control and nonlinear filtering for nondegenerate diffusion processes. Stochastics 8(1):63–77.CrossrefGoogle Scholar
  • Harris TE (1963) The Theory of Branching Processes. Die Grundlehren der Mathematischen Wissenschaften (Springer, Berlin).CrossrefGoogle Scholar
  • Hernández-Lerma O, Lasserre JB (1996) Discrete-Time Markov Control Processes (Springer, New York).CrossrefGoogle Scholar
  • Iscoe I, Ney P, Nummelin E (1985) Large deviations of uniformly recurrent Markov additive processes. Adv. Appl. Math. 6(4):373–412.CrossrefGoogle Scholar
  • Kantas N (2009) Sequential decision making in general state space models. Ph.D. thesis, University of Cambridge, Cambridge, UK.Google Scholar
  • Kappen HJ (2005) Linear theory for control of nonlinear stochastic systems. Physical Rev. Lett. 95(20):200201.CrossrefGoogle Scholar
  • Kleptsyna ML, Veretennikov AY (2008) On discrete time ergodic filters with wrong initial data. Probab. Theory Related Fields 141(3–4):411–444.CrossrefGoogle Scholar
  • Kolmogorov AN (1938) Zur lösung einer biologischen aufgabe. Comm. Math. Mech. Chebyshev Univ. Tomsk 2(1):1–12.Google Scholar
  • Kontoyiannis I, Meyn SP (2003) Spectral theory and limit theorems for geometrically ergodic Markov processes. Ann. Appl. Probab. 13(1):304–362.CrossrefGoogle Scholar
  • Makrini ME, Jourdain B, Lelièvre T (2007) Diffusion Monte Carlo method: Numerical analysis in a simple case. ESAIM: Mathematical Modelling and Numerical Analysis-Modélisation Mathématique et Analyse Numérique 41(2):189–213.CrossrefGoogle Scholar
  • Meyn S, Tweedie RL (2009) Markov Chains and Stochastic Stability, 2nd ed. (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Ney P, Nummelin E (1987) Markov additive processes I. Eigenvalue properties and limit theorems. Ann. Probab. 15(2):561–592.CrossrefGoogle Scholar
  • Nummelin E (2004) General Irreducible Markov Chains and Non-Negative Operators. Cambridge Tracts in Mathematics (Cambridge University Press, Cambridge, UK).Google Scholar
  • Rogers LCG, Williams D (1996) Diffusions, Markov processes and martingales: Vol. 1, foundations. J. Roy. Statist. Soc.-Ser. A Statist. Soc. 159(2):343.Google Scholar
  • Rousset M (2006) On the control of an interacting particle estimation of Schrödinger ground states. SIAM J. Math. Anal. 38(3):824–844.CrossrefGoogle Scholar
  • Sheu SJ (1984) Stochastic control and principal eigenvalue. Stochastics 11(3–4):191–211.CrossrefGoogle Scholar
  • Theodorou E, Buchli J, Schaal S (2010) A generalized path integral control approach to reinforcement learning. J. Machine Learn. Res. 11:3137–3181.Google Scholar
  • Todorov E (2008) General duality between optimal control and estimation. Proc. 47th IEEE Conf. Decision and Control, ’08. (IEEE, Piscataway, NJ), 4286–4292.CrossrefGoogle Scholar
  • Whiteley N (2013) Stability properties of some particle filters. Ann. Appl. Probab. 23(6):2500–2537.CrossrefGoogle Scholar
  • Whiteley N, Kantas N, Jasra A (2012) Linear variance bounds for particle approximations of time-homogeneous Feynman-Kac formulae. Stochastic Processes Their Appl. 122(4):1840–1865.CrossrefGoogle Scholar
  • Yaglom AM (1947) Certain limit theorems of the theory of branching random processes. Doklady Akad. Nauk SSSR (NS) 56:795–798.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.