Notes for Self Study

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References

Andral, C., and Kamatani, K. (2024). Automated techniques for efficient sampling of piecewise-deterministic markov processes.

Bafumi, J., Gelman, A., Park, D. K., and Kaplan, N. (2005). Practical Issues in Implementing and Understanding Bayesian Ideal Point Estimation. Political Analysis, 13(2), 171–187.

Corbella, A., Spencer, S. E. F., and Roberts, G. O. (2022). Automatic Zig-Zag Sampling in Practice. Statistics and Computing, 32(6), 107.

Dai, H., Pollock, M., and Roberts, G. (2019). Monte Carlo Fusion. Journal of Applied Probability, 56(1), 174–191.

Deming, W. E., and Stephan, F. F. (1940). On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. The Annals of Mathematical Statistics, 11(4), 427–444.

Fearnhead, P., Latuszynski, K., Roberts, G. O., and Sermaidis, G. (2017). Continious-time importance sampling: Monte carlo methods which avoid time-discretisation error.

Gelman, A. (2005). Analysis of variance—why it is more important than ever. The Annals of Statistics, 33(1), 1–53.

Hastings, W. K. (1970). Monte Carlo Sampling Methods Using Markov Chains and Their Applications. Biometrika, 57(1), 97–109.

Lorenz, E. N. (1963). Deterministic nonperiodic flow. Journal of Atmospheric Sciences, 20(2), 130–141.

Lorenz, E. N. (1995). Predictability: A problem partly solved. In Seminar on predictability, 4-8 september 1995. ECMWF.

Nemeth, C., and Fearnhead, P. (2021). Stochastic gradient markov chain monte carlo. Journal of the American Statistical Association, 116(533), 433–450.

Parisi, G., and Wu, Y. (1981). PERTURBATION THEORY WITHOUT GAUGE FIXING. Scientia Sinica, 24(4), 483–.

Rouder, J. N., Morey, R. D., Speckman, P. L., and Province, J. M. (2012). Default bayes factors for ANOVA designs. Journal of Mathematical Psychology, 56(5), 356–374.

Shi, Y., De Bortoli, V., Deligiannidis, G., and Doucet, A. (2022). Conditional simulation using diffusion Schrödinger bridges. In J. Cussens and K. Zhang, editors, Proceedings of the thirty-eighth conference on uncertainty in artificial intelligence,Vol. 180, pages 1792–1802. PMLR.

Sutton, M., and Fearnhead, P. (2023). Concave-Convex PDMP-based Sampling. Journal of Computational and Graphical Statistics, 32(4), 1425–1435.

Vargas, F., Grathwohl, W. S., and Doucet, A. (2023). Denoising Diffusion Samplers. In The eleventh international conference on learning representations.