Name: No likelihood? No problem! — Bayesian Statistics in R
Start: 2025-02-27T18:00:00Z
End: 2025-02-27T19:30:00Z
Location: LG.11 40 George Square

No likelihood? No problem! — Bayesian Statistics in R
Date: Thursday 27 February 2025, 6.00PM - 7.30PM
Location: LG.11 40 George Square, The University of Edinburgh, EH8 9LX
https://www.accessable.co.uk/the-university-of-edinburgh/central-area/access-guides/lg-11-teaching-room
https://www.openstreetmap.org/way/5325200

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\#\# Isabella Deutsch: R You Ready for ABC? Likelihood\-Free Bayesian Inference in Action\.

\[Isabella Deutsch\]\(https://isabelladeutsch\.com/\) recently completed a PhD in Statistics at the University of Edinburgh and now works as a Data Scientist at Smart Data Foundry\. She is passionate about being a translator between people and data\, such as at her show at the Fringe 2024\.

Approximate Bayesian Computation (ABC) offers an intuitive -- and likelihood-free -- way to talk about Bayesian Statistics. We investigate the use of ABC in the context of point processes with missing data, demonstrating how it can approximate posterior distributions using simulation alone. Drawing from real-world applications, we highlight the strengths and limitations of ABC in practice. Finally, we explore some code snippets that show how easy it is to get started with ABC in R.

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\#\# Jordan Richards: Fast and Amortised Bayesian Inference with NeuralEstimators

\[Jordan Richards\]\(https://jbrich95\.github\.io/\) is a lecturer of statistics in the School of Mathematics at The University of Edinburgh\. His main research interest is extreme value analysis\, and its intersection with spatial statistics and machine learning\.

Neural estimators are neural networks that transform data into parameter point estimates. These estimators are likelihood-free and amortised, in the sense that, after an initial setup cost, inference from observed data can be made in a fraction of the time required by conventional approaches, e.g., MCMC or maximum likelihood estimation. They are also approximate Bayes estimators and, therefore, are often referred to as neural Bayes estimators. We present the user-friendly R package NeuralEstimators, which interfaces with the Julia package NeuralEstimators.jl, and allows for the development and application of neural Bayes estimators. This package caters for any model for which simulation is feasible by allowing the user to implicitly define their model via simulated data. No likelihood or long MCMC chains required!

Mike Spencer

Ö. Ozan Evkaya

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No likelihood? No problem! — Bayesian Statistics in R

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