The paper develops efficient methods for estimating posterior distributions with few expensive likelihood evaluations.
problem Estimating posterior distributions with limited expensive likelihood evaluations.
method Active Bayesian regression using Gaussian processes for query efficiency.
result The proposed methods significantly reduce the number of likelihood evaluations needed for posterior estimation.
Improved MCMC sampling for expensive, irregular likelihoods.
problem Bayesian inference challenges with irregular, expensive likelihoods.
method Adapt subset samplers, introduce data-driven proxies, adaptive controller.
result Improved HINTS algorithm achieves best sampling error in fixed budget.
Adaptive Gaussian process approximates Bayesian inference for costly likelihoods.
problem Bayesian inference with computationally expensive likelihood functions.
method Gaussian process approximation with active learning design points.
result Competitive performance compared to existing methods for Bayesian computation.
Noise-Contrastive Estimation improves efficiency for estimating log-likelihood of complex point processes.
problem Estimating log-likelihood of complex multivariate point processes is computationally expensive.
method Noise-Contrastive Estimation adapted for multivariate point processes, with provable guarantees.
result Our method achieves similar log-likelihood with fewer evaluations and less time.
In this paper we present an algorithm for rapid Bayesian analysis that combines the benefits of nested sampling and artificial neural networks. The blind accelerated multimodal Bayesian inference (BAMBI) algorithm implements the MultiNest package for nested sampling as well as the training of an artificial neural netwo…
Bayesian inference for expensive likelihoods using Langevin Monte Carlo with NF.
problem Sampling from complex posterior distributions with expensive likelihoods.
method Deterministic Langevin equation with NF gradient, Metropolis-Hastings updates.
result Competitive performance compared to state-of-the-art methods.
VBMC combines variational inference and Bayesian quadrature for efficient posterior and model evidence estimation.
problem Efficient inference for models with expensive, black-box likelihoods.
method Combines variational inference with Gaussian-process based active-sampling Bayesian quadrature.
result Produces both a nonparametric approximation of the posterior and an approximate lower bound of the model evidence efficiently.
New method validates approximate likelihood and emulator models for expensive simulations.
problem Validation of approximate likelihood and emulator models for computationally intensive simulations.
method Statistical framework using two-sample and global goodness-of-fit tests.
result Can distinguish misspecified models and identify inadequate regions.
New method uses path signatures for efficient likelihood estimation in time-series data.
problem Intractable likelihood functions in complex dynamic models.
method Kernel classifier based on path signatures for sequential data.
result Path signatures yield highly performant classifiers, even with low sample numbers.
New method uses Gaussian ODE filtering to approximate likelihoods for fast ODE inverse problems.
problem Intractable forward models in likelihood-free inference, especially for ODEs.
method Gaussian ODE filtering to construct local Gaussian likelihood approximations.
result New solvers outperform standard likelihood-free approaches on benchmark systems.
A new concordance loss improves model performance and reliability in survival prediction.
problem Inconsistent evaluation of deep survival models using likelihood losses.
method Proposed a value-monotone concordance loss (SCL) to improve reliability and optimization.
result SCL achieves comparable discrimination and is the best or within one standard deviation of the best C-index across multiple datasets.
NoFAS combines variational inference and adaptive surrogate models for efficient inference of computationally expensive models.
problem Efficient inference of parameters from data with computationally expensive models.
method Variational inference with normalizing flow and adaptive surrogate model training.
result NoFAS reduces computational cost without sacrificing inferential accuracy.
New SMC samplers improve stochastic optimisation efficiency.
problem Optimizing functions with intractable gradients in machine learning and statistics.
method Sequential Monte Carlo (SMC) samplers for stochastic optimisation.
result Significant computational gains achieved with SMC approximations.
Paper proposes energy objective for training normalizing flows without determinants.
problem Challenges in training normalizing flows due to Jacobian determinants.
method Introduces energy objective based on proper scoring rules, determinant-free.
result Energy objective supports novel model families and competitive performance.
Improved diffusion sampling for inverse problems with faster and more robust inference.
problem High computational cost and lack of robustness in diffusion posterior sampling.
method Amortized variational inference with explicit likelihood guidance.
result Improved trade-off between inference speed and robustness to unseen degradations.
MolGAN generates valid small molecular graphs without graph matching.
problem Generating valid small molecular graphs efficiently.
method Adapts GANs to generate graph-structured data with reinforcement learning.
result MolGAN generates close to 100% valid compounds.
A new method normalizes EBM training by introducing a learnable parameter.
problem Training energy-based models with maximum likelihood is challenging due to intractable normalisation constants.
method Proposes a self-normalised log-likelihood (SNL) objective that introduces a learnable parameter representing the normalisation constant.
result The SNL objective is a lower bound of the log-likelihood and can be directly optimised using stochastic gradient techniques.
wBSL uses whitening transformations to speed up BSL for intractable likelihood models.
problem Computational demands of Bayesian synthetic likelihood with growing summary statistics.
method Whitening transformations to decorrelate summary statistics.
result Significant reduction in model simulations required for accurate inference.
DeepLR constructs confidence intervals for neural networks with asymmetric expansions.
problem Uncertainty estimation for neural network predictions.
method Likelihood-ratio-based approach for constructing asymmetric confidence intervals.
result DeepLR offers asymmetric intervals expanding in regions with limited data.
Efficient Bayesian decision-making with intractable likelihoods.
problem Bayesian decision-making under intractable likelihoods.
method Learning surrogate models and using simulation-based inference and Bayesian optimization.
result Optimal actions can be learned with fewer simulations than posterior inference.
Paper tackles noisy and expensive likelihoods in complex models.
problem Calibrating parameters in complex models with noisy and expensive evaluations.
method Ensemble Kalman methods and Langevin-based methods for inverse problems.
result Ensemble Kalman methods perform well in noisy conditions, while Langevin methods are adversely affected.
Neural likelihood approximates integer time series data efficiently.
problem Inference of parameters for integer-valued stochastic processes is challenging.
method Constructs a neural likelihood approximation for inference of parameters from time series data.
result Accurately approximates the true posterior with significant computational speed-ups.
JANA trains networks to approximate Bayesian models efficiently.
problem Intractable likelihood functions and posterior densities in Bayesian models.
method End-to-end training of three networks: summary, posterior, and likelihood networks.
result JANA provides accurate amortized marginal likelihood and posterior predictive estimation.
A novel rejection sampling step improves variational inference for latent variable models.
problem High variance in gradient estimates for approximate posterior in stochastic variational inference.
method Rejection sampling to discard low-likelihood samples and a new gradient estimator.
result Improves marginal log-likelihood estimation by 3.71 nats and 0.21 nats.
Enhanced MH algorithm reduces expensive function evaluations and improves sampling efficiency.
problem Computational expense of evaluating target distributions or likelihood functions, especially with big data.
method Accelerated MH algorithm using Bayesian optimization and Gaussian processes.
result Significant improvement in sampling efficiency and reduced function evaluations.
Improved MMD estimator for likelihood-free inference.
problem Computational challenges in estimating MMD for likelihood-free inference.
method Optimally-weighted MMD estimator with improved sample complexity.
result Significantly improved sample complexity for accurate MMD estimation.
Improves synthetic likelihood and ABC methods using bootstrapping.
problem Efficient Bayesian inference for computationally expensive models.
method Uses bootstrapping to improve synthetic likelihood estimates with fewer simulations.
result Accurately approximates posterior distributions with fewer model simulations.
Normalizing flow regression approximates posterior distributions without additional sampling.
problem Bayesian inference with computationally expensive likelihood evaluations.
method Normalizing flow regression (NFR) for offline inference.
result NFR yields a tractable posterior approximation through regression on existing log-density evaluations.
This work improves neural likelihood surrogates for stochastic models with a score-augmented loss.
problem Efficient parameter inference for stochastic models with computationally expensive likelihood functions.
method Score-augmented loss function for neural network likelihood surrogates.
result Improves surrogate quality at a lower computational cost compared to generating more data.
A new method for inference without likelihood, using logistic regression.
problem Statistical inference in the absence of a likelihood function.
method Estimate the ratio of data generating and marginal distributions using logistic regression.
result Automatic selection of relevant summary statistics.
Paper proposes efficient training for normalizing flows in Boltzmann generators.
problem Training normalizing flows for Boltzmann generators is computationally challenging and unstable.
method Regression Training of Normalizing Flows (RegFlow) using ℓ2-regression. result RegFlow enables efficient and stable training of normalizing flows for Boltzmann generators.
A new path gradient estimator speeds up normalizing flows without sacrificing accuracy.
problem High computational cost and limited scalability of path gradient estimators for normalizing flows.
method Proposed a fast path gradient estimator that improves computational efficiency and scalability.
result The new estimator achieves superior performance and reduced variance across various applications.
EG-LF-MCMC infers posterior densities without likelihoods.
problem Posterior inference for models with intractable likelihoods.
method Two-phase approach: error recording and classification for MCMC.
result EG-LF-MCMC provides approximate posterior densities efficiently.
An efficient LDP protocol for QMLE with improved practicality and theoretical guarantees.
problem Difficult implementation of existing LDP QMLE for large-scale surveys.
method Developed an alternative LDP protocol without long waiting time, high communication cost, and derivative boundedness assumptions.
result Sufficient conditions for consistency and asymptotic normality of the protocol.
New method for state inference in state-space models with unknown dynamics.
problem State inference in state-space models with computationally expensive and undefined dynamics.
method Estimate state transition dynamics using a multi-output Gaussian process and Bayesian Neural Network as a surrogate model.
result Significant improvement in accuracy for state inference and prediction in non-stationary user models.
This paper improves ABC-SMC by using a cheap simulator to reduce computational cost.
problem High computational cost of exact simulators in ABC.
method Delayed acceptance Markov chain Monte Carlo (MCMC) within ABC-SMC.
result The approach reduces computational cost without sacrificing accuracy.
Enhances SBI accuracy with multilevel Monte Carlo for expensive simulators.
problem Limited accuracy in SBI due to expensive simulators.
method Multilevel Monte Carlo techniques for cost-effective SBI.
result Significant enhancement in SBI accuracy with fixed computational budget.
Robust Bayesian Optimization using Student-t Likelihood for noisy data.
problem Outliers in Gaussian process models bias Bayesian Optimization.
method Student-t likelihood to segregate and robustly handle outliers.
result Improved exploration and efficiency in Bayesian Optimization.
Learning in restricted Boltzmann machine is typically hard due to the computation of gradients of log-likelihood function. To describe the network state statistics of the restricted Boltzmann machine, we develop an advanced mean field theory based on the Bethe approximation. Our theory provides an efficient message pas…
New method estimates HMM hidden states efficiently.
problem Inaccurate posterior predictive distribution in HMMs.
method Autoregressive-flow for estimating hidden states.
result Estimates comparable to SMC algorithm.
Spectral methods improve efficiency in nonparametric model inference.
problem Efficient inference for nonparametric models like IBP and HDP.
method Spectral methods for Indian Buffet Process and Hierarchical Dirichlet Process.
result Spectral methods provide computationally and statistically efficient inference.
Bayesian approach sparsifies neural networks efficiently.
problem Efficiently pruning neural networks to save resources.
method Sparsifiability via the Marginal likelihood (SpaM) framework.
result Prunes neural networks effectively without significant loss in performance.
PyVBMC speeds up Bayesian inference for expensive models in Python.
problem Efficient Bayesian inference for computationally expensive models.
method Variational Bayesian Monte Carlo (VBMC) algorithm.
result PyVBMC provides a flexible and efficient method for parameter estimation and model assessment.
Stochastic Stein Discrepancies improve inference efficiency.
problem Intractable computation of Stein discrepancies.
method Subsampled approximations of Stein operators.
result Stochastic Stein Discrepancies inherit convergence properties of standard SDs.
VAEs improve representation learning by inverting the data-generating process through self-consistency.
problem VAEs struggle to invert the data-generating process, yet often succeed in representation learning.
method Studied VAEs in the limit of near-deterministic decoders, proving self-consistency and showing ELBO convergence to a regularized log-likelihood.
result VAEs can perform independent mechanism analysis (IMA), recovering true latent factors under specific conditions.
Improved Gaussian Neural Processes for efficient multi-dimensional predictions.
problem Inability to model dependencies in outputs limits CNPs and NPs applicability.
method Proposes a new approach to model output dependencies using latent variables for maximum likelihood training, scalable to 2D and 3D data.
result Proposed models show good performance in synthetic experiments.
Automated model selection using Bayesian quadrature improves efficiency.
problem Slow convergence and unreliability of Monte Carlo methods for model comparison.
method Automated algorithm maximizing mutual information between posterior probability and model likelihoods.
result More accurate model posterior estimates with fewer likelihood evaluations.
A new method uses ABC-SMC to infer hybrid models in bioprocesses with limited data.
problem Inference of hybrid models in bioprocesses with limited real data and high uncertainties.
method Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) and linear Gaussian dynamic Bayesian network (LG-DBN) for posterior distribution approximation.
result The method accelerates hybrid model inference and supports process monitoring and robust control.