Raissi, M., Perdikaris, P. and Karniadakis, G.E., 2019. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational physics, 378, pp.686-707. Yang, L., Meng, X. and Karniadakis, G.E., 2021. B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data. Journal of Computational Physics, 425, p.109913. Lu, L., Pestourie, R., Yao, W., Wang, Z., Verdugo, F. and Johnson, S.G., 2021. Physics-informed neural networks with hard constraints for inverse design. SIAM Journal on Scientific Computing, 43(6), pp.B1105-B1132.

E. Nalisnick, J. M. Hernández-Lobato, P. Smyth, Dropout as a Structured Shrinkage Prior. In `International Conference on Machine Learning\', PMLR, 2019, 4712-4722. C. Louizos, K. Ullrich, M. Welling, Bayesian Compression for Deep Learning. In `Advances in Neural Information Processing Systems\', 2017, 3288-3298. K. Mohammad, D. Nielsen, V. Tangkaratt, W. Lin, Y. Gal, A. Srivastava, Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. In `International Conference on Machine Learning\', PMLR, 2018, 2611-2620.

[1] A. H. Sayed, “Adaptation, learning, and optimization over networks,” Found. Trends Mach. Learn., vol. 7, no. 4–5, pp. 311–801, 2014.
[2] A. H. Sayed, “Adaptive Networks,” Proc. IEEE, vol. 102, no. 4, pp. 460–497, Apr. 2014.
[3] R. Nassif, S. Vlaski, C. Richard, J. Chen, and A. H. Sayed, “Multitask Learning Over Graphs: An Approach for Distributed, Streaming Machine Learning,” IEEE Signal Process. Mag., vol. 37, no. 3, pp. 14–25, May 2020
[4] K. Dedecius and P. M. Djuric, “Sequential Estimation and Diffusion of Information Over Networks: A Bayesian Approach With Exponential Family of Distributions,” IEEE Trans. Signal Process., vol. 65, no. 7, pp. 1795–1809, Apr. 2017.

1. Kárný Miroslav, Guy Tatiana V.: On dynamic decision-making scenarios with multiple participants. In: Multiple Participant Decision Making. (Andrýsek J., Kárný M., Kracík J. eds.). ( International Series on Advanced Intelligence. 9). Advanced Knowledge International, Adelaide 2004, pp. 17-28.
2. Kárný Miroslav, Guy Tatiana V.: Fully probabilistic control design, Systems & Control Letters, 55:4, 259-265, 2006
3. Kárný Miroslav et al, Optimized Bayesian Dynamic Advising: Theory and Algorithms, Springer, London, 2006
4. Kárný Miroslav, Guy Tatiana V., Bodini A., Ruggeri F.: Cooperation via sharing of probabilistic information, Int. Journal of Computational Intelligence Studies, p. 139-162, 2009
5. Kárný Miroslav: Axiomatisation of Fully Probabilistic Design Revisited , Systems and Control Letters vol.141, 2020
6. Kárný Miroslav, Guy Tatiana V.: Preference Elicitation within Framework of Fully Probabilistic Design of Decision Strategies , IFAC-PapersOnLine. Volume 52, Issue 29 - Proceedings of the 13th IFAC Workshop on Adaptive and Learning Control Systems 2019, p. 239-244, IFAC Workshop on Adaptive and Learning Control Systems 2019 /13./, 2019
7. Kárný Miroslav: Prescriptive Inductive Operations On Probabilities Serving to Decision-Making Agents, IEEE Trans. on Systems Man Cybernetics-Systems vol.52, 4 p. 2110-2120, 2022

Doporučená literatura (části vybrané po dohodě se školitelem)

1. V. Peterka, Bayesian System Identification, in P. Eykhoff "Trends and Progress in System Identification", Pergamon Press, Oxford, 239-304, 1981.
2. M. Puterman, Markov decision processes, John Wiley & Sons, 1994.
3. D. P. Bertsekas, Dynamic Programming, Prentice Hall, 1987.
4. A. E. Eiben, S. K. Smit, Parameter tuning for configuring and analyzing evolutionary algorithms, Swarm and Evolutionary Computation 1 (2011) 19–31.
5. M. Kárný, Towards On-Line Tuning of Adaptive-Agent's Multivariate Meta-Parameter, Pattern Recognition Letters, 150, 170-175, 2021

Doporučená literatura (části vybrané po dohodě se školitelem)

1. V. Peterka, Bayesian System Identification, in P. Eykhoff "Trends and Progress in System Identification", Pergamon Press, Oxford, 239-304, 1981.
2. C. Marchetti. Action curves and clockwork geniuses. Technical report, IASA, Laxenburg, Vienna, 1984.
3. T. Braun, W. Ganzel, and A. Schubert. Scientometric indicators. World Scientific, (1985).
4. J. Mingers and L. Leydesdor_. A review of theory and practice in scientometrics. European Journal of Operational Research, 246 (2015),1-19.

1. M. Hutchinson, H. Oh, W. Chen, A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors. Information Fusion 36, 2017, 130-148.
2. P. Seibert and A. Frank, Source-receptor matrix calculation with a Lagrangian particle dispersion model in backward mode. Atmospheric Chemistry and Physics 4(1), 2004, 51–63.
3. V. Šmídl, A. Quinn, The Variational Bayes Method in Signal Processing. Springer, 2006.
4. O. Tichý, V. Šmídl, R. Hofman, K. Šindelářová, M. Hýža, A. Stohl, Bayesian inverse modeling and source location of an unintended I-131 release in Europe in the fall of 2011, Atmospheric Chemistry and Physics 17(20), 2017, 12677-12696.
5. O. Tichý, V. Šmídl, N. Evangeliou, Source term determination with elastic plume bias correction, Journal of Hazardous Materials vol.425 (2022), 127776.

Doporučená literatura (části vybrané po dohodě se školitelem)

1. V. Peterka, Bayesian System Identification, in P. Eykhoff "Trends and Progress in System Identification", Pergamon Press, Oxford, 239-304, 1981.
2. R. Kulhavý, M.B. Zarrop, On a General Concept of Forgetting, International Journal of Control 58(4), 905-924, 1993.
3. M. Kárný, Minimum Expected Relative Entropy Principle, Proc. of the 18th European Control Conference, 35-40, 2020.
4. M. Kárný, Approximate Bayesian recursive estimation, Inf. Sciences 285(1), 100-111 2014.