Neural Density Estimation and Likelihood-free Inference

Abstract

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.

PhD thesis submitted to the University of Edinburgh in April 2019. Includes in full the following articles: arXiv:1605.06376, arXiv:1705.07057, arXiv:1805.07226

References in corpus (51)

• Auto-Encoding Variational Bayes
• ADADELTA: An Adaptive Learning Rate Method
• A Brief Introduction to PYTHIA 8.1
• Conditional Image Generation with PixelCNN Decoders
• Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
• On the Convergence of Adam and Beyond
• Pixel Recurrent Neural Networks
• Progressive Growing of GANs for Improved Quality, Stability, and Variation
• Expectation Propagation for approximate Bayesian inference
• Density estimation using Real NVP
• Adaptive approximate Bayesian computation
• Online EM Algorithm for Latent Data Models
• Non-linear regression models for Approximate Bayesian Computation
• Variational Dropout and the Local Reparameterization Trick
• A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation
• TensorFlow Distributions
• Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
• Validating Bayesian Inference Algorithms with Simulation-Based Calibration
• Fast likelihood-free cosmology with neural density estimators and active learning
• Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
• Mining gold from implicit models to improve likelihood-free inference
• Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology
• Invertible Residual Networks
• WAIC, but Why? Generative Ensembles for Robust Anomaly Detection
• Towards a Neural Statistician
• Fast $\epsilon$-free Inference of Simulation Models with Bayesian Conditional Density Estimation
• Automatic physical inference with information maximising neural networks
• GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
• Likelihood-free Markov chain Monte Carlo
• Likelihood-free inference with emulator networks
• Automatic Variational Inference in Stan
• Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models
• Learning and Querying Fast Generative Models for Reinforcement Learning
• Variational Bayes with Synthetic Likelihood
• Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs
• A Comparison of Methods for Computing Autocorrelation Time
• Comparison of Maximum Likelihood and GAN-based training of Real NVPs
• A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
• Monge-Ampère Flow for Generative Modeling
• Inference Compilation and Universal Probabilistic Programming
• FloWaveNet : A Generative Flow for Raw Audio
• Block Neural Autoregressive Flow
• Likelihood-free inference with an improved cross-entropy estimator
• Transformation Autoregressive Networks
• On Bayesian inference for the M/G/1 queue with efficient MCMC sampling
• Recognition Networks for Approximate Inference in BN20 Networks
• Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference
• Deep Diffeomorphic Normalizing Flows
• Probabilistic programs for inferring the goals of autonomous agents
• Optimal proposals for Approximate Bayesian Computation
• Automatic Variational ABC

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