New method for efficient inference over complex parameter spaces.
problem Challenges in Bayesian inference for high-dimensional, intractable likelihoods.
method Arbitrary Marginal Neural Ratio Estimation (AMNRE) for simulation-based inference.
result Efficient inference over arbitrary subsets of parameters without numerical integration.
Method infers parameters in complex diffusion processes.
problem Parameter inference in high-dimensional, non-linear diffusion processes.
method Differentiable score matching to approximate diffusion bridges, used in an importance sampler.
result Numerically stable framework for parameter inference and diffusion mean estimation.
Bayesian models that mix multiple Dirichlet prior parameters, called Multi-Dirichlet priors (MD) in this paper, are gaining popularity. Inferring mixing weights and parameters of mixed prior distributions seems tricky, as sums over Dirichlet parameters complicate the joint distribution of model parameters. This paper s…
PENN neural network estimates parameter distributions for econ models.
problem Lack of interpretability in deep neural networks for econ applications.
method Generative neural network architecture for Bayesian inference.
result PENN provides interpretable parameter estimates and visualizations.
EFI automates statistical inference for big data.
problem Statistical inference for model parameters based on observations.
method EFI uses stochastic gradient Markov chain Monte Carlo and sparse deep neural networks.
result EFI provides higher fidelity in parameter estimation and automates the inference process.
The paper develops scalable variational inference for Bayesian neural networks under model and parameter uncertainty.
problem Combining structural and parameter uncertainties in scalable Bayesian neural networks.
method Adapted variational inference with reparametrization for model space constraints.
result Comparable accuracy with sparse inference compared to ordinary BNNs.
Framework synthesizes programs for simulating complex models and estimating parameters.
problem Parameter estimation for complex models requires manual encoding of fixed model structures.
method Combines LLMs for program synthesis with neural simulation-based inference.
result Identifies plausible model families from open-ended prompts with high accuracy.
Paper efficiently infers differential parameters in time-varying models using time score matching.
problem Efficiently inferring differential parameters in time-varying probabilistic models.
method Directly estimates the differential parameter using time score matching and proves consistency of the method.
result Consistent estimation of parameter derivatives in high-dimensional settings.
Parameter inference in ordinary differential equations is an important problem in many applied sciences and in engineering, especially in a data-scarce setting. In this work, we introduce a novel generative modeling approach based on constrained Gaussian processes and leverage it to build a computationally and data eff…
Type system captures CI relationships for probabilistic models.
problem Challenges in inference for models with mixed discrete and continuous parameters.
method Information flow type system for probabilistic programming.
result Well-typed programs guarantee certain CI relationships.
Improves learning of spectral mixture kernels with approximate Bayesian inference.
problem Difficult optimization of large number of SM kernel parameters.
method Approximate Bayesian inference using variational distribution of spectral points and random Fourier features.
result Accelerates convergence and leads to better optimal parameters.
JADAI optimizes design and inference for parameter estimation.
problem Parameter estimation with active optimization of design variables.
method Jointly trains a policy, history network, and inference network to minimize posterior error.
result Achieves superior or competitive performance across benchmarks.
This work uses variational inference to estimate parameters of opinion dynamics models.
problem Challenges in parameter estimation for ABMs of social phenomena.
method Transformed ABM parameter estimation into an optimization problem using variational inference.
result Estimates parameters more accurately than simulation-based and MCMC methods.
New method speeds up Bayesian inference for complex simulators.
problem Challenges in Bayesian inference for complex stochastic simulators with intractable likelihood functions.
method Optimization Monte Carlo framework reformulated as deterministic optimization problems with gradient-based methods.
result Accurate posterior inference with reduced runtimes compared to existing methods.
Likelihood-free inference for simulator-based statistical models has developed rapidly from its infancy to a useful tool for practitioners. However, models with more than a handful of parameters still generally remain a challenge for the Approximate Bayesian Computation (ABC) based inference. To advance the possibiliti…
Parameter inference for stochastic differential equations is challenging due to the presence of a latent diffusion process. Working with an Euler-Maruyama discretisation for the diffusion, we use variational inference to jointly learn the parameters and the diffusion paths. We use a standard mean-field variational appr…
SBI uses neural networks to infer model parameters from simulators.
problem Computational infeasibility of Bayesian inference for complex models.
method Training neural networks on simulator-generated data.
result Efficient Bayesian inference without likelihood evaluations.
Likelihood-free inference is concerned with the estimation of the parameters of a non-differentiable stochastic simulator that best reproduce real observations. In the absence of a likelihood function, most of the existing inference methods optimize the simulator parameters through a handcrafted iterative procedure tha…
Hybrid approach combines VI and HMC for efficient Bayesian inference in neural networks.
problem Computational demands and inaccuracies in Bayesian inference for neural networks.
method Combines VI and HMC, reducing parameter space and accelerating inference.
result Significantly reduces inference time for large neural networks, improving uncertainty quantification.
ACE improves GBI for simulators by approximating cost functions, making inference more efficient.
problem Inference for misspecified simulators is overly restrictive.
method Amortized cost estimation (ACE) for Generalized Bayesian Inference (GBI).
result ACE provides accurate cost predictions and more efficient inference.
Proposes new methods for inference in GLMs without assuming model correctness.
problem Inference for GLMs assumes model correctness, leading to uncertainty and bias.
method Develops nonparametric estimands and uses influence curves with flexible procedures.
result Inference for GLM parameters is improved without model correctness assumptions.
SBI helps scientists match model outputs to data.
problem Tuning simulator parameters to match empirical data is hard.
method Simulation-based inference (SBI) identifies compatible parameter sets.
result SBI quantifies parameter uncertainty by identifying high-probability regions.
HNPE uses auxiliary data to estimate parameters in uncertain models.
problem Uncertain models with identical observations.
method Exploits global parameters from auxiliary data to estimate parameters.
result Validated on a motivating example and applied to neuroscience.
LSBI approximates likelihood with linear functions for cosmological parameter estimation.
problem Estimating cosmological parameters from complex data.
method Sequential Linear Simulation-based Inference (LSBI) using Gaussian approximations.
result LSBI achieves convergence after 4-5 rounds of simulations, comparable to neural methods.
New Bayesian method for joint sparse parameter inference.
problem Inference of jointly sparse parameter vectors from multiple measurements.
method Hierarchical Bayesian learning with joint sparsity-promoting priors.
result New algorithms consistently outperform existing methods in numerical experiments.
New method uses transport maps for efficient Bayesian inference.
problem Efficiently perform sequential Bayesian inference of static model parameters.
method Estimation of structured transport maps to extract conditional distributions.
result Gradient-based characterization of posterior density for online parameter estimation.
Researchers develop methods for inference in hierarchical models using neural simulations.
problem Inference in hierarchical models with intractable likelihoods.
method Construct neural estimators for likelihood-ratio or posterior, accounting for hierarchical structure.
result Explicitly accounting for hierarchical structure leads to tighter parameter constraints.
A new method infers parameters from PDEs using Gaussian processes.
problem Estimating unknown parameters in PDEs from noisy data.
method PDE-Informed Gaussian Process (PIGP) method.
result The method bypasses numerical solvers for PDEs and provides uncertainty quantification.
This paper addresses parameter estimation for wave equations with Markovian switching.
problem Parameter estimation for wave equations with abrupt changes.
method Bayesian statistical framework using discrete sparse Bayesian learning.
result Strong performance in parameter estimation for variable coefficient PDEs.
We apply variational inference to learn vehicle trajectory parameters from noisy data.
problem Learning parameters for vehicle trajectory estimation from noisy measurements.
method Gaussian variational inference with parameter learning in a motion and sensor model context.
result High-quality state estimates achieved even with outliers and false loop closures.
New method uses low-fidelity simulations to efficiently infer parameters of high-fidelity models.
problem Challenges in inferring parameters of computationally expensive high-fidelity models.
method Multifidelity simulation-based inference using transfer learning and adaptive selection of high-fidelity parameters.
result Significant reduction in the number of high-fidelity simulations required for inference.
PriorGuide adapts diffusion models to new priors at test time.
problem Limited applicability of prior distributions in diffusion-based inference.
method PriorGuide uses a guidance approximation to adapt diffusion models to new priors at test time.
result Enhances the versatility of pre-trained inference models by allowing flexible adaptation to new priors.
A scalable method for accurate inference of low-dimensional parameters in high-dimensional linear regression.
problem Statistical inference for low-dimensional parameters in high-dimensional linear regression models.
method Mean-field variational Bayes approach, focusing on nuisance parameters and conditional distributions.
result Competitive numerical performance and theoretical guarantees for estimation and uncertainty quantification.
Improves SVGP methods for faster and more accurate Gaussian process inference.
problem Efficient non-conjugate Gaussian process inference.
method Dual parameterization of SVGP methods using site parameters.
result Faster and more accurate inference with tighter evidence lower bound.
New PG samplers improve inference in coupled state-space models.
problem Bayesian inference from multiple time series with shared parameters.
method Marginalized Particle Gibbs samplers for coupled state-space models.
result Improved parameter inference through shared information.
Improves scalability and efficiency of mixture models in black-box variational inference.
problem Scaling mixture models in black-box variational inference leads to high parameter and time costs.
method Introduces MISVAE for amortized mixture parameter space and new ELBO estimators.
result Achieves superior estimation performance with fewer parameters and shorter inference time.
This work simplifies Bayesian inference for neural networks by identifying influential parameter directions.
problem High computational complexity in Bayesian inference for neural networks due to high-dimensional parameter space.
method Constructing an active subspace of influential parameter directions to reduce dimensionality.
result Effective and scalable Bayesian inference achieved via reduced active subspace.
A new meta-learning method using shared variational inference.
problem Meta-learning with uncertainty over model parameters.
method Shared amortized variational inference network for conditional prior and posterior.
result Prevents collapse of conditional prior to Dirac delta function.
MINIMALIST maximizes mutual information for likelihood estimation from simulated data.
problem Learning model parameters from likelihood functions that cannot be computed.
method Maximizes mutual information between simulated data and model parameters using neural networks.
result Different methods aiming at the same optimal energy form can be directly benchmarked.
ALFI improves likelihood-free inference for black-box generators.
problem Limitations of likelihood-free inference on black-box generators.
method Adversarial Likelihood-Free Inference (ALFI) to estimate posterior distributions.
result ALFI achieves best parameter estimation accuracy with limited simulation.
Diffusion models enhance SBI with flexible parameter and observation learning.
problem Efficient and accurate estimation of latent parameters from simulations and real data.
method Score-based diffusion models, guidance, score composition, flow matching, consistency models, joint modeling.
result Flexibility and versatility in modeling various problems.
Unified inference framework for spatiotemporal data.
problem Challenges in extracting mechanistic insights from complex spatiotemporal data.
method Vision transformer-driven variational encoding and likelihood-free Bayesian approach.
result Unified inference framework for identifying spatial and temporal patterns.
Stochastic VB improves nonlinear model inference speed and accuracy.
problem Bayesian inference of nonlinear models from noisy data.
method Stochastic Variational Bayesian (VB) inference for nonlinear models.
result Stochastic VB achieves comparable parameter recovery to analytical solution but is faster.
Bayesian model tackles high-dimensional inverse problems efficiently.
problem Estimating spatially-varying parameters in expensive models.
method Multiscale Bayesian inference with deep generative models and MCMC.
result Efficient estimation of global and local parameter features.
PAVI speeds up Bayesian inference for large datasets.
problem Challenges in Bayesian inference for large population studies.
method Plate-amortized Variational Inference (PAVI) that shares parameterization across i.i.d. variables.
result Significant speedup in training variational distributions, orders of magnitude faster.
This study uses deep learning to infer stellar parameters from short TESS and K2 observations.
problem Inferring precise stellar parameters from short-duration TESS and K2 observations.
method Developed a machine learning algorithm to infer asteroseismic parameters from one-month-long TESS observations of red giants.
result The algorithm can accurately infer Δν and νmax for approximately 50% of TESS samples and ΔΠ1 for about 200 young red-giants from K2. We study the performance of the adaptive construction scheme for a Bayesian inference on the Quadratic GARCH model which introduces the asymmetry in time series dynamics. In the adaptive construction scheme a proposal density in the Metropolis-Hastings algorithm is constructed adaptively by changing the parameters of t…
Paper offers anytime-valid inference for causal parameters using DML.
problem Classic DML is only valid asymptotically for a fixed sample size.
method Time-uniform DML results for anytime-valid inference.
result Valid inference at any arbitrary stopping time.