May 2025: Interactive charts and fast Bayesian inference

* Title: May 2025: Interactive charts and fast Bayesian inference
* Date: Thursday 15 May 2025, 6.00PM - 7.00PM
* Location: 2.11 Appleton Tower (https://www.accessable.co.uk/the-university-of-edinburgh/central-area/access-guides/2-11-seminar-room) The University of Edinburgh, EH8 9LE (https://www.openstreetmap.org/way/514395905)

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## Nicola Rennie: Interactive charts in R and beyond

Nicola Rennie (https://nrennie.rbind.io) is a data visualisation specialist, who has been making charts and modelling data in R for over 10 years. She has a background in statistics, co-authored the Royal Statistical Society’s Best Practices for Data Visualisation guidance, and is Secretary of the Royal Statistical Society’s Teaching Statistics Section.

In this session, we’ll be talking about different ways that you can make interactive charts using R, or a combination of R and other tools. You’ll see examples of creating tooltips and dropdown menus - all browser-based, no server required!
You might see examples from the following packages:
* Plotly
* ggiraph (with a little bit of Javascript)
* Observable JS (with Quarto)
* D3 (including r2d3)

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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!