Tweets about Bayesian statistics, Monte Carlo, etc from a Reader in Statistics at the University of Warwick. Personal account.
Coventry, UK
Joined November 2010
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Richard Everitt retweeted
📢 Post-Bayesian online seminar series coming!📢
To stay posted, sign up at
tinyurl.com/postBayes
We'll discuss cutting-edge methods for posteriors that no longer rely on Bayes Theorem.
(e.g., PAC-Bayes, generalised Bayes, Martingale posteriors, ...)
Pls circulate widely!
Richard Everitt retweeted
The first talk of Season 6 is next week! On 31st October, 11am GMT, Ullrich Köthe (University of Heidelberg) will talk about "Free-form flows for physics-informed generative modelling". The talk will be on MS teams. Sign up to the list to get the link: listserv.csv.warwick.ac.uk/m…
Richard Everitt retweeted
This is the new account for the One World Approximate Bayesian Inference (ABI) Seminar. Please follow if you are interested in any flavour of approximate Bayesian inference, including simulation-based inference. Website: warwick.ac.uk/fac/sci/statis…
Richard Everitt retweeted
Do you think you know Maximum Likelihood? Think again
It seems like such a natural idea, but there’s an epic and turbulent history with numerous assaults on the core idea, culminating in a beautiful and complicated theory.
A highly entertaining account:
arxiv.org/abs/0804.2996
Richard Everitt retweeted
Congratulations to David Huk, Yuanhe Zhang, Mark Steel, Ritabrata Dutta (all Warwick Statistics), whose paper "Quasi-Bayes meets Vines" (arxiv.org/abs/2406.12764) has been accepted for #NeurIPS ! In the paper they model high-dimensional densities with the Quasi-Bayesian Vine.
Richard Everitt retweeted
#NeurIPS decisions are out! Congratulations to Jen Ning Lim (Warwick Statistics PhD Student) who has
had a paper on Semi-implicit Variational Inference (preprint:
arxiv.org/abs/2407.00649) accepted for a spotlight
presentation at NeurIPS 2024.
Richard Everitt retweeted
Two new chapters from my free online book in human genetics out this weekend!
These complete Part 3 of the book, on human population structure and history:
3.3: Inferring human prehistory from genetic data [this thread]
3.4: Ancient DNA [next thread]
web.stanford.edu/group/pritc…
I'm delighted to release the first half of my new open-access online textbook in human population genetics:
web.stanford.edu/group/pritc…
Richard Everitt retweeted
I'm delighted to release the first half of my new open-access online textbook in human population genetics:
web.stanford.edu/group/pritc…
Richard Everitt retweeted
ERC Starting Grant for Tom Berrett
Tom Berrett has been announced as a recipient of an ERC Starting Grant worth €1.5 million.
Information on Tom's research can be found on his website here: thomasberrett.github.io
Richard Everitt retweeted
Newborn (extended abstract) paper in Bayesian Experimental Design (BED). This specific iteration takes the same approach as in arxiv.org/abs/2402.07868: formulate sequential planning as an amortized inference procedure, but differs on its computational treatment. 1/7
Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Design ift.tt/Z0VLw8q
Richard Everitt retweeted
Last week to apply for machine learning postdoc positions in my team in Manchester or Helsinki
Machine learning postdoc positions in my team in Helsinki, Finland, and Manchester, UK; DL September 15, 2024
Aalto University, Helsinki: aalto.fi/en/open-positions/p…
University of Manchester, UK: jobs.manchester.ac.uk/Job/Jo…
Some more info about the team: kaski-lab.com
Richard Everitt retweeted
It took me SEVEN years to figure out that you can save a ggplot as a vectorized .svg file ggsave("file.svg"). The svg file can be opened on powerpoint and it will be completely vectorized, even text are still recognized as text boxes! No more recreating plots on illustrator!!!!!
Richard Everitt retweeted
Announcing: AI in Industry and Academia Conversations
ailab.criteo.com/ai-in-indus…
Conversations with interesting people about machine learning and AI in industry and academia. Starting 19 September.
Featuring: @DrMaggiori, Nicolas Chopin, @dawen_liang , @flavianv , David Rohde,..
Richard Everitt retweeted
Adaptive MCMC can be very useful in practice, but theoretical results are technical. We attempted to write a more accessible story about adaptive MCMC theory: arxiv.org/abs/2408.14903. It starts from the beautiful martingale decomposition of Andrieu & Moulines (2006):
Richard Everitt retweeted
Warwick University is advertising two Assistant Professor positions, each of which is joint between Maths and Life Sciences. The SBIDER group in Warwick is friendly, collegiate, and an exciting place to build a research group. Come join us! warwick-careers.tal.net/vx/a…
Richard Everitt retweeted
We are now hiring into our new Biostatistical Machine Learning theme @MRC_BSU ! We are looking for: (1) a Junior Group Leader in AI/ML for Biomedicine (w/ startup package including an RA) who will help shape the theme; and (2) an RA to join my group. Details below. Please RT :)
📢 Our exciting new Biostatistical Machine Learning theme is recruiting for 2 new roles 👇
Independent Junior Group Leader
jobs.cam.ac.uk/job/48012/
Research Associate
jobs.cam.ac.uk/job/48013/
#MachineLearning #ArtificialIntelligence #DataScience @pauldwkirk @Cambridge_Uni
Richard Everitt retweeted
If you thought that
(1) the ‘cold posterior effect’ is unique to Bayesian deep learning or
(2) the temperature in cold posteriors / power posteriors can be tuned to significantly improve the predictive capabilities of a (regular) model,
think again.
arxiv.org/abs/2408.08806
Richard Everitt retweeted
2/ Paper info
📄 We discuss the @arxiv preprint "Bayesian Inference of Reproduction Number from Epidemiological and Genetic Data Using Particle MCMC"
👥 Authors: Alicia Gill, Jere Koskela, @XavierDidelot, @bayesian_stats (Richard Everitt)
🔗: doi.org/10.48550/arXiv.2311.…
Richard Everitt retweeted
1/ 🎧 New SBIDER Presents podcast episode! 🎧@warwickengages #SciComm
🗣️ @EdMHill & @LGuzmanRincon discuss with @warwickstats Alicia Gill work on particle MCMC methods to infer the time-varying reproduction number from epidemiological & genetic data.
🔗: podbean.com/eas/pb-yy45r-16a…