# Neural Density Estimation and Likelihood-free Inference

arXiv: https://arxiv.org/abs/1910.13233 · HTML: https://mlsanity.com/paper/1910.13233/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2019.

## Built on by (most cited first, 23 of 23)

- [Solving Schr"odinger Bridges via Maximum Likelihood](https://mlsanity.com/paper/2106.02081/) (2021)
- [Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference](https://mlsanity.com/paper/2310.16975/) (2023)
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- [Squared Neural Families: A New Class of Tractable Density Models](https://mlsanity.com/paper/2305.13552/) (2023)
- [Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation](https://mlsanity.com/paper/2103.14068/) (2021)
- [m^ast of two-dimensional electron gas: a neural canonical transformation study](https://mlsanity.com/paper/2201.03156/) (2022)
- [Unifying Simulation and Inference with Normalizing Flows](https://mlsanity.com/paper/2404.18992/) (2024)
- [Paired Autoencoders for Likelihood-free Estimation in Inverse Problems](https://mlsanity.com/paper/2405.13220/) (2024)
- [Unsupervised tree boosting for learning probability distributions](https://mlsanity.com/paper/2101.11083/) (2021)
- [Efficient identification of informative features in simulation-based inference](https://mlsanity.com/paper/2210.11915/) (2022)
- [Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networks](https://mlsanity.com/paper/2311.11913/) (2023)
- [Machine-Learned Exclusion Limits without Binning](https://mlsanity.com/paper/2211.04806/) (2022)
- [Approximation properties of slice-matching operators](https://mlsanity.com/paper/2310.10869/) (2023)
- [Deep Optimal Experimental Design for Parameter Estimation Problems](https://mlsanity.com/paper/2406.14003/) (2024)
- [Data-Driven High-Dimensional Statistical Inference with Generative Models](https://mlsanity.com/paper/2506.06438/) (2025)
- [Who Should I Engage with At What Time? A Missing Event Aware Temporal Graph Neural Network](https://mlsanity.com/paper/2301.08399/) (2023)
- [Deep Modeling of Non-Gaussian Aleatoric Uncertainty](https://mlsanity.com/paper/2405.20513/) (2024)
- [Feature Alignment as a Generative Process](https://mlsanity.com/paper/2106.12562/) (2021)
- [Identification of Vehicle Dynamics Parameters Using Simulation-based Inference](https://mlsanity.com/paper/2108.12114/) (2021)
- [Universal Approximation and the Topological Neural Network](https://mlsanity.com/paper/2305.16639/) (2023)
- [Dark Energy Survey Year 3 results: Simulation-based wCDM inference from weak lensing and galaxy clustering maps with deep learning: Analysis design](https://mlsanity.com/paper/2511.04681/) (2025)
- [Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2603.09490/) (2026)
- [A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems](https://mlsanity.com/paper/2607.06252/) (2026)

## Abstract (arXiv)

I consider two problems in machine learning and statistics: the problem of estimating the joint probability density of a collection of random variables, known as density estimation, and the problem of inferring model parameters when their likelihood is intractable, known as likelihood-free inference. The contribution of the thesis is a set of new methods for addressing these problems that are based on recent advances in neural networks and deep learning.