Bibilography#
Sudipto Banerjee, Bradley P Carlin, and Alan E Gelfand. Hierarchical modeling and analysis for spatial data. Chapman and Hall/CRC, 2003.
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Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul A. Szerlip, Paul Horsfall, and Noah D. Goodman. Pyro: deep universal probabilistic programming. J. Mach. Learn. Res., 20:28:1–28:6, 2019. URL: http://jmlr.org/papers/v20/18-403.html.
David M Blei. Build, compute, critique, repeat: data analysis with latent variable models. Annual Review of Statistics and Its Application, 1(1):203–232, 2014.
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang. JAX: composable transformations of Python+NumPy programs. 2018. URL: jax-ml/jax.
Andrew Gelman. Data analysis using regression and multilevel/hierarchical models. Cambridge university press, 2007.
Andrew Gelman, Aki Vehtari, Daniel Simpson, Charles C Margossian, Bob Carpenter, Yuling Yao, Lauren Kennedy, Jonah Gabry, Paul-Christian Bürkner, and Martin Modrák. Bayesian workflow. arXiv preprint arXiv:2011.01808, 2020.
Nicholas Metropolis and Stanislaw Ulam. The monte carlo method. Journal of the American statistical association, 44(247):335–341, 1949.
Kevin P Murphy. Probabilistic machine learning: Advanced topics. MIT press, 2023.
Du Phan, Neeraj Pradhan, and Martin Jankowiak. Composable effects for flexible and accelerated probabilistic programming in numpyro. arXiv preprint arXiv:1912.11554, 2019.
C.P. Robert and G. Casella. Monte Carlo statistical methods. Springer Verlag, 2004.
Christian P Robert, George Casella, Christian P Robert, and George Casella. The metropolis—hastings algorithm. Monte Carlo statistical methods, pages 267–320, 2004.
Daniel Sanz-Alonso and Omar Al-Ghattas. A first course in monte carlo methods. arXiv preprint arXiv:2405.16359, 2024.
Robert H Shumway, David S Stoffer, and David S Stoffer. Time series analysis and its applications. Volume 3. Springer, 2000.
Aki Vehtari, Andrew Gelman, and Jonah Gabry. Practical bayesian model evaluation using leave-one-out cross-validation and waic. Statistics and computing, 27:1413–1432, 2017.
Christopher KI Williams and Carl Edward Rasmussen. Gaussian processes for machine learning. Volume 2. MIT press Cambridge, MA, 2006.
Erik Štrumbelj, Alexandre Bouchard-Côté, Jukka Corander, Andrew Gelman, Håvard Rue, Lawrence Murray, Henri Pesonen, Martyn Plummer, and Aki Vehtari. Past, present and future of software for bayesian inference. Statistical Science, 39(1):46–61, 2024.