Referências
Dorigo, M., V. Maniezzo e A. Colorni (1996). “Ant System: Optimization by a Colony of Cooperating Agents”. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 26(1), p. 29–41. doi:10.1109/3477.484436.
Dorigo, M. e T. Stützle (2004). Ant Colony Optimization. Cambridge: MIT Press.
Durrett, R. (2016). Essentials of Stochastic Processes. 3. ed. Cham: Springer. doi:10.1007/978-3-319-45614-0.
Geman, S. e D. Geman (1984). “Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images”. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-6(6), p. 721–741. doi:10.1109/TPAMI.1984.4767596.
Hastings, W. K. (1970). “Monte Carlo sampling methods using Markov chains and their applications”. Biometrika 57(1), p. 97–109. doi:10.1093/biomet/57.1.97.
Hertz, J. A., A. S. Krogh e R. G. Palmer (1991). Introduction to the Theory of Neural Computation. Redwood City: Addison-Wesley.
Hinton, G. E. (2002). “Training Products of Experts by Minimizing Contrastive Divergence”. Neural Computation 14(8), p. 1771–1800. doi:10.1162/089976602760128018.
Hopfield, J. J. (1982). “Neural networks and physical systems with emergent collective computational abilities”. Proceedings of the National Academy of Sciences 79(8), p. 2554–2558. doi:10.1073/pnas.79.8.2554.
Jackson, J. R. (1957). “Networks of Waiting Lines”. Operations Research 5(4), p. 518–521. doi:10.1287/opre.5.4.518.
Jukes, T. H. e C. R. Cantor (1969). “Evolution of Protein Molecules”. Em: Mammalian Protein Metabolism. Vol. 3. New York: Academic Press.
Kelly, F. P. (1979). Reversibility and Stochastic Networks. Chichester: John Wiley & Sons. https://www.statslab.cam.ac.uk/~fpk1/rsn.html.
Kingman, J. F. C. (1993). Poisson Processes. Oxford Studies in Probability 3. Oxford: Oxford University Press. https://research-information.bris.ac.uk/en/publications/poisson-processes/.
Lawler, G. F. (2006). Introduction to Stochastic Processes. 2. ed. Boca Raton: Chapman e Hall/CRC.
Le Cam, L. (1960). “An approximation theorem for the Poisson binomial distribution”. Pacific Journal of Mathematics 10(4), p. 1181–1197. https://msp.org/pjm/1960/10-4/pjm-v10-n4-p11-p.pdf.
Levin, D. A. e Y. Peres (2017). Markov Chains and Mixing Times. 2. ed. Providence: American Mathematical Society. Com contribuições de Elizabeth L. Wilmer. doi:10.1090/mbk/107.
Metropolis, N., A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller e E. Teller (1953). “Equation of State Calculations by Fast Computing Machines”. The Journal of Chemical Physics 21(6), p. 1087–1092. doi:10.1063/1.1699114.
Page, L., S. Brin, R. Motwani e T. Winograd (1999). The PageRank Citation Ranking: Bringing Order to the Web. Stanford InfoLab. http://ilpubs.stanford.edu:8090/422/.
Pemantle, R. (2007). “A survey of random processes with reinforcement”. Probability Surveys 4, p. 1–79. doi:10.1214/07-PS094.
Privault, N. (2024). Discrete Stochastic Processes: Tools for Machine Learning and Data Science. Cham: Springer. doi:10.1007/978-3-031-65820-4.
Rabiner, L. R. (1989). “A tutorial on hidden Markov models and selected applications in speech recognition”. Proceedings of the IEEE 77(2), p. 257–286. doi:10.1109/5.18626.
Rasmussen, C. E. e C. K. I. Williams (2006). Gaussian Processes for Machine Learning. Adaptive Computation and Machine Learning. Cambridge: MIT Press. https://gaussianprocess.org/gpml/.
Ross, S. M. (2007). Introduction to Probability Models. 9. ed. Academic Press.
Schinazi, R. B. (1999). Classical and Spatial Stochastic Processes. Boston: Birkhäuser. doi:10.1007/978-1-4612-1582-0.
Ville, J. (1939). Étude critique de la notion de collectif. Thèses de l'entre-deux-guerres 218. https://numdam.org/item/THESE_1939__218__1_0/.
Wald, A. (1947). Sequential Analysis. New York: John Wiley & Sons.
Wolff, R. W. (1982). “Poisson Arrivals See Time Averages”. Operations Research 30(2), p. 223–231. doi:10.1287/opre.30.2.223.
Zucchini, W., I. L. MacDonald e R. Langrock (2016). Hidden Markov Models for Time Series: An Introduction Using R. 2. ed. Boca Raton: CRC Press.