A benchmark for simulation-based inference methods.
problem Lack of a public benchmark for 'likelihood-free' algorithms.
method Provided a benchmark with tasks and performance metrics, including neural networks and ABC methods.
result State-of-the-art algorithms have room for improvement, and neural network-based approaches generally perform better.
Simulation-based inference methods can produce unreliable posterior approximations.
problem Reliability of simulation-based inference methods for scientific use cases.
method Benchmarked algorithms including Neural Posterior Estimation, Neural Ratio Estimation, Sequential Neural Likelihood, and Approximate Bayesian Computation.
result Ensembling posterior surrogates provides more reliable approximations.
pmsims R package uses Gaussian process for flexible sample size estimation in clinical models.
problem Determining adequate sample size for clinical prediction models.
method Simulation-based Gaussian process search for flexible sample size estimation.
result Gaussian process-based method produces more stable sample size estimates, especially in challenging settings.
ConDiSim uses diffusion models to approximate complex system posteriors efficiently.
problem Simulation-based inference of systems with intractable likelihoods.
method Conditional diffusion model with forward and reverse processes.
result Effective posterior approximation across various benchmark and real-world problems.
A new algorithm improves efficiency and robustness of heuristic optimization in simulation-based problems.
problem Optimizing input parameters for stochastic simulation-based optimization.
method Reactive sample size algorithm based on parametric tests and indifference-zone selection.
result The reactive method improves efficiency and robustness of heuristic optimization techniques.
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.
This work extends balancing to various simulation-based inference algorithms for more conservative posterior approximations.
problem Overconfident posterior approximations in simulation-based inference.
method Introduces a balanced version of neural posterior estimation and contrastive neural ratio estimation.
result Balanced versions tend to produce conservative posterior approximations on various benchmarks.
Bayesian neural networks improve simulation-based inference with limited data.
problem Inaccurate inference in data-poor regimes with limited or expensive simulations.
method Bayesian neural networks for posterior approximation, accounting for computational uncertainty.
result Bayesian neural networks produce well-calibrated posteriors with few simulations.
Unified framework for simulation-based inference learns a single model for multiple tasks.
problem Simulation-based inference for multiple tasks with limited model retraining.
method Unified flow-matching generative model with query-aware masking distribution.
result Competitive performance on various inference tasks and real-world problems.
A new method optimizes a generalized Kullback-Leibler divergence for better simulation-based inference.
problem Optimizing likelihood functions when they are only known implicitly.
method Optimizes a generalized Kullback-Leibler divergence that accounts for normalization constants in unnormalized distributions.
result Unified approach that combines Neural Posterior Estimation and Neural Ratio Estimation.
This study benchmarks likelihood-free inference methods for models with heavy-tailed or discrete data.
problem Comparing likelihood-free inference methods for models with structural features like heavy-tails or discreteness.
method Four approaches: MLE, NBE, EOT, and AW-NBE are evaluated using simulations.
result The choice of evaluation tools is crucial for models with extremes and discrete data.
SNVI combines likelihood estimation with variational inference for efficient Bayesian inference.
problem Bayesian inference in models with intractable likelihoods.
method Sequential Neural Variational Inference (SNVI) that combines likelihood-estimation with variational inference.
result SNVI is more computationally efficient than previous algorithms without sacrificing accuracy.
BNRE improves simulation-based inference by producing more conservative posteriors.
problem Overconfident posteriors from current simulation-based inference algorithms risk false inferences.
method Balanced Neural Ratio Estimation (BNRE) that produces more conservative posterior approximations.
result BNRE produces more conservative posterior surrogates on all tested benchmarks and simulation budgets.
CP4SBI improves the calibration of credible sets in SBI models.
problem Inaccurate credible sets in SBI models lead to underestimation of true parameters.
method Develops a local conformal calibration framework for SBI models.
result Improves the quality of uncertainty quantification for neural posterior estimators.
Simformer uses transformer models to perform flexible Bayesian inference.
problem Current simulation-based inference methods are inflexible and require fixed priors.
method Trains a probabilistic diffusion model with transformer architectures.
result Outperforms state-of-the-art methods on various benchmarks.
Sensitivity analysis for individualized effects in OTRs with binary risk factors.
problem Addressing omitted confounding in individualized effects of OTRs.
method Simulation-based sensitivity analysis to simulate unmeasured confounders.
result Benchmarking the strength of omitted confounding for binary risk factors.
Improved inference efficiency for complex simulations.
problem Challenges in performing inference under resource-intensive stochastic simulators.
method Active sequential neural posterior estimation (ASNPE) integrating active learning into posterior estimation.
result Improved sample efficiency with low computational overhead.
NQE uses quantile regression for fast SBI with cubic Hermite splines.
problem Efficient Bayesian inference for complex models with limited data.
method Neural Quantile Estimation (NQE) learns quantiles autoregressively and interpolates them using cubic Hermite splines.
result NQE achieves state-of-the-art performance on various benchmark problems.
GATSBI uses GANs for SBI, improving posterior estimation in high dimensions.
problem Statistical inference on stochastic models without likelihoods.
method Adversarial approach to variational objective, amortized inference, implicit priors.
result GATSBI returns well-calibrated posterior estimates in high dimensions.
New method boosts BOED using SBI and neural likelihood.
problem Maximizing EIG in BOED with intractable likelihood.
method Neural likelihood estimation, multi-start gradient ascent.
result Significantly improved BOED performance over state-of-the-art.
Improved SBI with neural networks for complex models.
problem Accurate inference for complex models with intractable likelihood.
method Structured mixtures of probability distributions for likelihood and posterior approximation.
result Accurate posterior inference with smaller computational footprint.
We analyze errors in filtering algorithms using optimal transport.
problem Estimation errors in optimal transport-based filtering algorithms.
method Systematic analysis of estimation errors for conditional Brenier maps.
result Demonstrates effectiveness and practical potential of the optimal transport filtering algorithm.
Paper introduces RVNP to improve SBI in misspecified models.
problem Misspecification in simulation-based inference leads to unreliable posterior estimation.
method RVNP uses variational inference and error modeling to bridge the simulation-to-reality gap.
result RVNP can recover robust posterior inference without hyperparameters or priors.
Misspecification-Aware Simulation-Based Inference via Side-Channel Guidance
problem Simulation-based inference (SBI) of latent parameters is hindered by simulator misspecification.
method Misspecification-Aware Simulation-Based Inference (MA-SBI) turns side-channel text into a posterior correction.
result MA-SBI matches the oracle posterior across 10 seeds and two backbones.
Efficiently estimates marginal posteriors for complex simulations.
problem Bayesian inference in high-dimensional, intractable likelihood scenarios.
method Simulates and estimates low-dimensional marginal posteriors, using truncated indicators.
result Simulator efficiency and robustness testing of inference results.
New method improves SBI efficiency and scalability.
problem Scalability issues in SBI methods for large datasets.
method Langevin dynamics with score matching, exploiting likelihood structure.
result Structured score network enhances statistical efficiency and scalability.
CMPE improves SBI efficiency and accuracy.
problem Efficiently infer parameters of complex simulation models.
method CMPE is a new conditional sampler that distills a continuous probability flow.
result CMPE outperforms current state-of-the-art algorithms on hard low-dimensional benchmarks and competitive on high-dimensional problems.
We tackle missing data in SBI methods and introduce a neural process approach.
problem Missing data in SBI methods can bias parameter estimation.
method We introduce a neural process approach to jointly learn imputation and inference.
result Our method provides robust inference outcomes compared to baselines.
New method calibrates neural SBI to avoid overconfident posteriors.
problem Overconfident posteriors in SBI due to inaccurate uncertainty quantification.
method Introduces a calibration term into neural model training objective, enabling end-to-end backpropagation.
result Achieves competitive or better coverage and posterior density than existing methods.
CausalRegNet generates accurate data for gene perturbation experiments, improving CSL methods.
problem Assessing and selecting causal structure learning methods in gene perturbation experiments.
method CausalRegNet, a multiplicative effect structural causal model, generates accurate observational and interventional data.
result CausalRegNet generates more accurate distributions and scales better than current simulation frameworks.
TSNPE improves SBI efficiency and scalability.
problem Efficient and scalable simulation-based inference for complex models.
method Sequential inference with truncated proposals.
result TSNPE performs on par with previous methods and scales to complex models.
DRO-NPE improves neural posterior estimation by reducing overconfidence and overfitting.
problem Overconfident and unreliable posteriors in simulation-based inference with limited simulation budgets.
method Distributionally robust approach using Wasserstein ambiguity set and KL-based metrics.
result Consistently improves coverage and calibration across benchmark tasks.
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.
New method uses neural networks to efficiently approximate Bayesian inference for complex models.
problem Efficiently approximating Bayesian inference for complex models with varying temperatures.
method Fully amortized neural posterior estimator trained on a single forward pass.
result Achieves competitive posterior approximations across various temperatures and benchmarks.
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.
This study benchmarks AI agents for personalized retail promotions using simulations.
problem Optimizing coupon targeting for sparse customer purchase events.
method Comprehensive simulations of customer shopping behaviors; training RL agents on batch data.
result Contextual bandit and deep RL methods outperform static policies in sparse reward environments.
Method trains neural network for optimal decisions from stochastic simulators.
problem Suboptimal decisions from SBI approximations of posterior distributions.
method Trains neural network on simulated data to predict optimal actions.
result Induces similar cost as true posterior for optimal actions.
Proposes a new method for likelihood-to-evidence ratio estimation.
problem Inherent bias in multiclass ratio estimation framework.
method Contrastive Neural Ratio Estimation (C-NeR) framework.
result C-NeR framework removes bias and improves diagnostic reliability.
FMCPE improves SBI accuracy by correcting posterior estimators with flow matching.
problem Model misspecification in SBI leads to biased or overconfident posteriors.
method Flow Matching Corrected Posterior Estimation (FMCPE) trains a posterior approximator and corrects it using calibration samples.
result FMCPE consistently mitigates misspecification effects, improving inference accuracy and uncertainty quantification.
L-C2ST improves local diagnostics for SBI approximations.
problem Evaluating trustworthiness of posterior approximations in SBI.
method Local evaluation of posterior estimators at any observation.
result Offers better statistical power and interpretability.
GenSBI offers JAX-native SBI methods for natural sciences.
problem Lack of native SBI libraries in JAX for natural sciences.
method Flow and diffusion generative models in JAX.
result Near-ideal mean C2ST scores on SBIBM tasks.
New method speeds up inference for tall data models.
problem Inference for complex models with tall data.
method Diffusion posterior sampling for simulation-based inference.
result Significantly faster and more stable inference.
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.
Python package for fast simulation-based inference.
problem Intractable likelihood functions in Bayesian inference.
method Uses neural networks as surrogate models for Bayesian inference.
result Highly efficient and user-friendly for constructing SBI estimators.
We construct realistic equity option market simulators based on generative adversarial networks (GANs). We consider recurrent and temporal convolutional architectures, and assess the impact of state compression. Option market simulators are highly relevant because they allow us to extend the limited real-world data set…
Simulation-based inference aids in predicting disease dynamics for health policy.
problem Predicting disease dynamics to inform public health interventions.
method Simulation-based inference using machine learning.
result Potential for efficient interpretable Bayesian inference in epidemiological models.
Self-consistency improves the accuracy of model comparison methods.
problem Improving the accuracy of model comparison methods when simulation models are misspecified.
method Supplement traditional simulation-based training with a self-consistency loss on unlabeled real data.
result Self-consistency training improves model comparison accuracy, especially in open-world scenarios.
Improved diffusion models for sampling from given distributions.
problem Training diffusion models to sample from a given distribution.
method Benchmarked and improved off-policy methods for diffusion sampling.
result A novel exploration strategy improves sample quality.