Handbook of Approximate Bayesian Computation

By Scott Anthony Sisson, Yanan Fan, MARK A. Beaumont. 2018

523 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability, Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Gilles Louppe
2. Michael U Gutmann
3. Jakob H. Macke
4. Samuel Kaski
5. Ritabrata Dutta
6. David S. Greenberg
7. Marcel Nonnenmacher
8. Jukka Corander

In Statistics and Probability

1. Jukka Corander
2. Michael U Gutmann
3. Samuel Kaski
4. Ritabrata Dutta
5. Marcel Nonnenmacher
6. David S. Greenberg
7. Gilles Louppe
8. Owen Thomas

In Probabilistic & Bayesian Methods

1. Gilles Louppe
2. Michael U Gutmann
3. Jakob H. Macke
4. Samuel Kaski
5. Ritabrata Dutta
6. David S. Greenberg
7. Marcel Nonnenmacher
8. Jukka Corander

Citations in the corpus

• Automatic Posterior Transformation for Likelihood-Free Inference (2019, methodology): “Classical approaches to such likelihood-free statistical inference (also known as Approximate Bayesian Computation (ABC), see Sisson et al., 2018) do not scale to high-dimensional applications, and typically rely on ad-hoc choices to design summary statistics and distance functions.”
• Dynamic Likelihood-free Inference via Ratio Estimation (DIRE) (2018, methodology): “The methods include approximate Bayesian computation (ABC, Tavar´e et al. 1997; Pritchard et al. 1999), synthetic likelihood (Wood 2010; Price et al. 2017) and its generalizations (Dutta et al. 2016; Fasiolo et al. 2018); for recent reviews, see e.g. (Lintusaari et al. 2017; Sisson et al. 2018).”
• Likelihood-Free Inference by Ratio Estimation (2016, methodology): “For a comprehensive introduction to the field, we refer the reader to the review papers by Beaumont (2010); Hartig et al. (2011); Marin et al. (2012); Lintusaari et al. (2017); Sisson et al. (2018).”
• Bayesian forecasting in economics and finance: A modern review (2022, methodology): “The methods in (iii) have been based on the principles of approximate Bayesian computation (ABC) (Marin et al., 2011; Sisson and Fan, 2011; Sisson et al., 2019), Bayesian synthetic likelihood (BSL) (Price et al., 2018), variational Bayes (VB) (Blei et al., 2017), and integrated nested Laplace…”
• Bayesian Synthetic Likelihood (2017, methodology): “While approximate Bayesian computation (ABC) (Sisson et al., 2018; Drovandi, 2017) is perhaps the most well-established likelihood-free method, there are many other approaches.”