Acknowledgements, links and literature#
Acknowledgements and links#
AIMS-SA and Ulrich Paquet personally for the invitation. Being invited to teach at AIMS is a privilege!
The students of the first cohort of the “AI for Science” Masters at AIMS South Africa. Out of 31 students, 28 opted to take the course. Plus, 13 students from Stellenbosch joined remotely. I value your trust!
Machine Learning and Global Health network for many things, but in particular for the (virtual, at the time) space where I learnt Numpyro through a reading group together with some MLGH members: Swapnil Mishra, Iwona Hawryluk, Tim Wolock, Theo Rashid, Giovanni Charles. And, of course, to Seth Flaxman who hired me at the time.
Deep Learning Indaba for showing me how much ML enthusiasm there is on the African continent and making me want to contribute
Co-authors of the paper Bayesian workflow for disease transmission modeling in Stan and all participants of the regular Thursday Stan call which enabled me to co-author.
Lorenzo Ciardo from Kellogg College at Oxford for telling me about the Buffon’s needle problem.
Richard McElreath for posting the prior-likelihood conflict example
Darren Wilkinson for the brilliant GP visualisation idea which he’s been presenting for years at GPSS.
Previous workshops on which I taught and which served as a basis fort this extended course:
AMLD 2020 workshop “Bayesian Inference: embracing uncertainty” with Julia + Turing.jl, R + Stan and Python+PyMC3.
Nordic ProbAI 2022 lecture on Bayesian workflow.
Kira Duesterwald and James Allingham for writing together the DLI-23 practical, which is used in the chapter on probability distributions and random variables.
Stan Lazic, whose reflections on his book writing experience made me realise that I, most likely, don’t want to extend these lecture notes to a book - too time consuming :)
STPH where my own journey into Bayesian inference and spatial statistics began.
Imperial College London, Epidemiology and Biostatistics department who recently hired me as a lecturer providing me with an opportunity to stay in academia and research for the long term.
Literature#
“Statistical Inference”, George Casella, Roger L. Berger
“Pattern Recognition and Machine Learning”, Christopher M. Bishop
“Information Theory, Inference, and Learning Algorithms”, David MacKay
“Probabilistic Machine Learning: An Introduction”, Kevin Murphy
“Bayesian Data Analysis”, Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin
“Statistical Rethinking”, Richard McElreath
“Bayesian optimisation”, Roman Garnett
“A Conceptual Introduction to Hamiltonian Monte Carlo”, Michael Betancourt
“Hierarchical Modeling and Analysis for Spatial Data”, Banerjee, Carlin, Gelfand
“Time Series Analysis and Its Applications: With R Examples”, Robert H. Shumway
Funding#
I acknowledge being supported in part by the AI2050 program at Schmidt Sciences (Grant [G-22-64476]).