Cover Image

An efficient likelihood-free Bayesian inference method based on sequential neural posterior estimation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xiong, Yifei, Yang, Xiliang, Zhang, Sanguo, He, Zhijian
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Computation
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Leveraging Nested MLMC for Sequential Neural Posterior Estimation with Intractable Likelihoods
by: Yang, Xiliang, et al.
Published: (2024)

Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs
by: Ghosh, Sanmitra, et al.
Published: (2024)

Simulation-based Bayesian Inference from Privacy Protected Data
by: Xiong, Yifei, et al.
Published: (2023)

Misspecification-robust likelihood-free inference in high dimensions
by: Thomas, Owen, et al.
Published: (2020)

A partial likelihood approach to tree-based density modeling and its application in Bayesian inference
by: Ma, Li, et al.
Published: (2024)

Sequential Kalman Tuning of the $t$-preconditioned Crank-Nicolson algorithm: efficient, adaptive and gradient-free inference for Bayesian inverse problems
by: Grumitt, Richard D. P., et al.
Published: (2024)

Approximation Error Upper and Lower Bounds for Hölder Class with Transformers
by: He, Xin, et al.
Published: (2026)

Demonstrating the power and flexibility of variational assumptions for amortized neural posterior estimation in environmental applications
by: Maceda, Elliot, et al.
Published: (2024)

Maximum likelihood inference for high-dimensional problems with multiaffine variable relations
by: Brouillon, Jean-Sébastien, et al.
Published: (2024)

Counterfactual inference in sequential experiments
by: Dwivedi, Raaz, et al.
Published: (2022)

Simulation-based Bayesian inference under model misspecification
by: Kelly, Ryan P., et al.
Published: (2025)

GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs
by: Jiao, Yuling, et al.
Published: (2023)

Gradient-based inference of abstract task representations for generalization in neural networks
by: Hummos, Ali, et al.
Published: (2024)

QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference
by: Kim, Taesu, et al.
Published: (2024)

Robust Fuzzy local k-plane clustering with mixture distance of hinge loss and L1 norm
by: Huang, Junjun, et al.
Published: (2026)

State estimations and noise identifications with intermittent corrupted observations via Bayesian variational inference
by: Sun, Peng, et al.
Published: (2026)

Learned harmonic mean estimation of the marginal likelihood for multimodal posteriors with flow matching
by: Polanska, Alicja, et al.
Published: (2026)

Dual-space posterior sampling for Bayesian inference in constrained inverse problems
by: Siahkoohi, Ali, et al.
Published: (2026)

Multilevel neural simulation-based inference
by: Hikida, Yuga, et al.
Published: (2025)

Conservative neural posterior estimation via distributionally robust training
by: Laplante, William, et al.
Published: (2026)

Mixed neural posterior estimation for simulators with discrete and continuous parameters
by: Boelts, Jan, et al.
Published: (2026)

Conditional diffusions for amortized neural posterior estimation
by: Chen, Tianyu, et al.
Published: (2024)

Generative diffusion posterior sampling for informative likelihoods
by: Zhao, Zheng
Published: (2025)

On multi-token prediction for efficient LLM inference
by: Mehra, Somesh, et al.
Published: (2025)

Bayesian posterior approximation with stochastic ensembles
by: Balabanov, Oleksandr, et al.
Published: (2022)

Theta-regularized Kriging: Modelling and Algorithms
by: Xie, Xuelin, et al.
Published: (2026)

Scalable Bayesian Structure Learning for Gaussian Graphical Models Using Marginal Pseudo-likelihood
by: Mohammadi, Reza, et al.
Published: (2023)

Beyond likelihood ratio bias: Nested multi-time-scale stochastic approximation for likelihood-free parameter estimation
by: Li, Zehao, et al.
Published: (2024)

Fast post-process Bayesian inference with Variational Sparse Bayesian Quadrature
by: Li, Chengkun, et al.
Published: (2023)

Student-t processes as infinite-width limits of posterior Bayesian neural networks
by: Caporali, Francesco, et al.
Published: (2025)

Model-agnostic information transfer and fusion for classification with label noise
by: Guojun, Zhu, et al.
Published: (2026)

The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparison
by: Piras, Davide, et al.
Published: (2024)

A more efficient method for large-sample model-free feature screening via multi-armed bandits
by: Ouyang, Xiaxue, et al.
Published: (2025)

CoLT: The conditional localization test for assessing the accuracy of neural posterior estimates
by: Chen, Tianyu, et al.
Published: (2025)

Approximate Bayesian inference for cumulative probit regression models
by: Aliverti, Emanuele
Published: (2025)

To discretize continually: Mean shift interacting particle systems for Bayesian inference
by: Belhadji, Ayoub, et al.
Published: (2026)

Distributed MCMC inference for Bayesian Non-Parametric Latent Block Model
by: Khoufache, Reda, et al.
Published: (2024)

Quasi-Bayesian sequential deconvolution
by: Favaro, Stefano, et al.
Published: (2024)

BlackJAX: Composable Bayesian inference in JAX
by: Cabezas, Alberto, et al.
Published: (2024)

Understanding and Improving Training-free Loss-based Diffusion Guidance
by: Shen, Yifei, et al.
Published: (2024)