Luke Hardcastle

Britain Lost 14,000 Third Places. They Were Called Pubs. Is Your Local Next? How private equity reshaped the local and the postcode tool that shows the pubs most at risk.

My brother wanted a London pub crawl. The result? My new Substack post: "Britain Lost 14,000 Third Places. They were Called Pubs. Is Your Local Next?" How private equity reshaped the local, which pubs are most at risk and most importantly what to do about it.
open.substack.com/pub/laurenle...

**Part 1: From Bayesian inference to Bayesian workflow** 1. Bayesian theory and Bayesian practice 2. Statistical modeling and workflow 3. Computational tools 4. Introduction to workflow: Modeling performance on a multiple choice exam **Part 2: Statistical workflow** 5. Building statistical models 6. Using simulations to capture uncertainty 7. Prediction, generalization, and causal inference 8. Visualizing and checking fitted models 9. Comparing and improving models 10. Statistical inference and scientific inference **Part 3: Computational workflow** 11. Fitting statistical models 12. Diagnosing and fixing problems with fitting 13. Approximate algorithms and approximate models 14. Simulation-based calibration checking 15. Statistical modeling as software development

**4. Case studies** 16. Coding a series of models: Simulated data of movie ratings 17. Prior specification for regression models: Reanalysis of a sleep study 18. Predictive model checking and comparison: Clinical trial 19. Building up to a hierarchical model: Coronavirus testing 20. Using a fitted model for decision analysis: Mixture model for time series competition 21. Posterior predictive checking: Stochastic learning in dogs 22. Incremental development and testing: Black cat adoptions 23. Debugging a model: World Cup football 24. Leave-one-out cross validation model checking and comparison: Roaches 25. Model building and expansion: Golf putting 26. Model building with latent variables: Markov models for animal movement 27. Model building: Time-series decomposition for birthdays 28. Models for regression coefficients and variable selection: Student grades 29. Sampling problems with latent variables: No vehicles in the park 30. Challenge of multimodality: Differential equation for planetary motion 31. Simulation-based calibration checking in model development workflow **Appendices** A. Statistical and computational workflow for Bayesians and non-Bayesians B. How to get the most out of Bayesian Data Analysis

Bayesian Workflow by
Andrew Gelman, Aki Vehtari, @rmcelreath.bsky.social with @danpsimpson.bsky.social, @charlesm993.bsky.social, @yulingy.bsky.social, Lauren Kennedy, Jonah Gabry, @paulbuerkner.com, @modrakm.bsky.social, @vianeylb.bsky.social

(in production, estimated copy-editing time 6 weeks)

Sebastiano Grazzi, Sifan Liu, Gareth O. Roberts, Jun Yang: Sub-Cauchy Sampling: Escaping the Dark Side of the Moon https://arxiv.org/abs/2601.11066 https://arxiv.org/pdf/2601.11066 https://arxiv.org/html/2601.11066

Caveat Vendor Universities, audience capture, and bullshit; Beowulf, Kendrick Lamar

I wrote about the endless temptation successful people feel to justify and feel justified in a clearly toxic system. Happy new year.

open.substack.com/pub/rottenan...

Usual MCMC algorithms are typically guaranteed to work well when used to sample from target distributions for which

i) mass is reasonably well-concentrated in the centre of the state space, and
ii) the log-density is smooth and of moderate growth.

Outside of this setting, things can go poorly.

“look how much better my favorite model is compared compared to these other TRASH MODELS😒😒😒 when I specifically chose a DGP that matches my models assumptions and not the others’😌”

Yes, yes you are. Stop cosplaying "legitimate concerns". When you join a far right rally, organised by well known far right leader Tommy Robinson, with a host of speakers who outright say they are far right, you are a racist. No ifs, buts or maybes. There are no excuses for being there.

Diffusion piecewise exponential models for survival extrapolation using Piecewise Deterministic Monte Carlo The piecewise exponential model is a flexible non-parametric approach for time-to-event data, but extrapolation beyond final observation times typically relies on random walk priors and deterministic ...

I'll be talking about my recent pre-print (with Sam Livingstone and Gianluca Baio) where we use a prior based on an underlying diffusion process to guide long-term extrapolations in survival models:

arxiv.org/abs/2505.05932

Posterior sampling uses PDMPs, hence the link to the workshop!

Newcastle Non-Reversible Sampling Workshop Topic: This workshop will bring together researchers at the forefront of modern sampling techniques and related disciplines, to discuss recent advances in the field. This event is funded by the ProbA...

In Newcastle this week attending what should be a very fun workshop on non-reversible sampling! If you are interested in any of the talks (including my own, tomorrow) there are links to join remotely on the conference website!

sites.google.com/view/probai-...

New paper on arXiv! And I think it's a good'un 😄

Meet the new Lattice Random Walk (LRW) discretisation for SDEs. It’s radically different from traditional methods like Euler-Maruyama (EM) in that each iteration can only move in discrete steps {-δₓ, 0, δₓ}.

I am pleased to announce that together with some friends, we are organising a workshop on Non-Reversible MCMC Sampling, taking place at Newcastle University from 8–10 September 2025.

Details on the programme and registration can be found at the workshop website (sites.google.com/view/probai-...).

Starting from last October, we (@OnlineMCSeminar on Twitter, sites.google.com/view/monte-c...) have been running an online seminar on all aspects of Monte Carlo methods, with about ~30 talks so far. We are currently paused for the summer, expecting to return in September 2025.