The article "Neural Methods for Amortized Inference" by Andrew Zammit-Mangion, Matthew Sainsbury-Dale, and Raphaël Huser reviews recent advancements in simulation-based inference methods that incorporate neural networks. These methods leverage the representational capacity of neural networks, optimization libraries, and graphics processing units to learn complex mappings between data and inferential targets, enabling rapid inference through feed-forward operations. The review covers point estimation, approximate Bayesian inference, summary-statistic construction, and likelihood approximation, highlighting the benefits of amortized inference over Markov chain Monte Carlo (MCMC) methods. The authors also discuss software tools and provide a practical illustration to showcase the wide array of available tools for amortized inference. The article concludes with an overview of relevant topics and future research directions, emphasizing the potential of neural networks in making Bayesian inference more efficient and accessible.The article "Neural Methods for Amortized Inference" by Andrew Zammit-Mangion, Matthew Sainsbury-Dale, and Raphaël Huser reviews recent advancements in simulation-based inference methods that incorporate neural networks. These methods leverage the representational capacity of neural networks, optimization libraries, and graphics processing units to learn complex mappings between data and inferential targets, enabling rapid inference through feed-forward operations. The review covers point estimation, approximate Bayesian inference, summary-statistic construction, and likelihood approximation, highlighting the benefits of amortized inference over Markov chain Monte Carlo (MCMC) methods. The authors also discuss software tools and provide a practical illustration to showcase the wide array of available tools for amortized inference. The article concludes with an overview of relevant topics and future research directions, emphasizing the potential of neural networks in making Bayesian inference more efficient and accessible.