The paper provides guidelines for choosing between SBI methods in complex biological models.
problem Choosing appropriate SBI methods for real-world biological data.
method Comprehensive guidelines and application to agent-based models.
result Statistical SBI methods outperform neural SBI methods with sufficient computational resources.
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.
Study reveals model misspecification significantly impacts neural SBI algorithms.
problem Impact of model misspecification on neural SBI algorithms.
method Comprehensive study of neural SBI algorithms under various forms of model misspecification.
result Misspecification profoundly deleterious to performance of neural SBI algorithms.
This work proposes validation diagnostics for SBI algorithms using Normalizing Flows.
problem Lack of appropriate validation methods for SBI algorithms with complex, high-dimensional data.
method Develops validation diagnostics based on Normalizing Flows with theoretical guarantees.
result Offers theoretical guarantees on consistency of NF-based estimators.
Dynamic SBI improves SBI efficiency without rounds, reducing simulation and training costs.
problem Efficiently perform complex scientific inference with high-dimensional data.
method Adaptive dataset transformation, parallel simulation and training.
result Significant improvements in simulation and training efficiency.
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.
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.
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.
Cost-aware SBI reduces expensive simulations in complex models.
problem High computational cost in simulating complex models.
method Combination of rejection and self-normalised importance sampling.
result Significant reduction in overall cost of inference.
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.
BOED improves SBI by optimizing experimental designs and inference functions.
problem Efficiently use experimental resources for better inference on complex models.
method Link mutual information bounds between SBI and BOED, optimizing both design and inference.
result BOED improves inference in real-world simulators in epidemiology and biology.
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 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.
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.
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.
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.
Paper tackles SBI under model misspecification, presenting robust strategies.
problem Challenges in SBI under model misspecification.
method Three key strategies: robust summary statistics, generalised Bayesian inference, and error modelling.
result Empirical results show vulnerabilities of SBI and effectiveness of misspecification-robust alternatives.
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.
SBI provides more accurate pole positions than chi-squared minimization in model misspecification.
problem Accurate pole position estimation in pi-pi scattering models.
method Simulation Based Inference (SBI) method compared to chi-squared minimization.
result SBI leads to more robust predictions of pole positions in models of pi-pi scattering.
Plug-in robust NPE method adapts summaries independently of pretrained NPE.
problem Misspecification of neural posterior estimators under test data distribution.
method Minimum-distance summaries using maximum mean discrepancy (MMD).
result Substantial robustness gains with minimal additional overhead.
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.
Review of diffusion models for SBI in non-ideal data scenarios.
problem Inference of parameters from complex simulation outputs with intractable likelihoods.
method Diffusion models for likelihood-free inference, addressing model misspecification, unstructured observations, and missing data.
result Improved robustness and efficiency in SBI methods for non-ideal data scenarios.
Proposes a method to improve SBI under model misspecification.
problem Unreliable inference from SBI methods under model misspecification.
method Introduces a regularized loss function to penalize statistics that increase model-data mismatch.
result Demonstrates superior performance and robust inference in misspecified scenarios.
New method improves sample-efficiency in neural posterior estimation using simulator gradients.
problem High-fidelity posterior estimation with complex physical simulations is time-consuming.
method Neural Posterior Estimation (NPE) with differentiable simulators and gradient information.
result Improves sample-efficiency in posterior density estimation.
Improved likelihood-free inference using preconditioned neural posterior estimation.
problem Inaccurate posterior estimation in likelihood-free inference methods.
method Preconditioned Neural Posterior Estimation (PNPE) and Sequential PNPE (PSNPE) methods.
result PNPE and PSNPE improve posterior estimation accuracy over NPE and SNPE.
The paper improves SBI for BHMs by diagnosing misspecification and inferring parameters.
problem Model misspecification in Bayesian hierarchical models.
method Two-step framework: latent function diagnosis followed by SBI of target parameters.
result Improved simulation-based inference for complex models without explicit model checking.
SBI improves uncertainty analysis of cardiovascular biomarkers.
problem Mapping waveforms back to plausible physiological parameters.
method Simulation-based inference (SBI) for statistical inference.
result Posterior distributions provide a multi-dimensional representation of uncertainty.
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.
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.
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.
The paper analyzes the error accumulation in a compositional score-based algorithm for SBI.
problem How to effectively combine multiple observations to improve parameter inference.
method Study of the GAUSS algorithm's compositional score and its mean squared error.
result Established an upper bound on the mean squared error of the compositional score.
E&E uses contrastive learning to speed up SBI for high-dimensional systems.
problem Challenges in training high-dimensional emulators for complex systems.
method Contrastive learning for low-dimensional latent embedding and fast emulator.
result Superior performance in non-identifiable parameter estimation 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.
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.
CogFormer trains a transformer to estimate parameters across various cognitive models.
problem Difficulty in fitting complex cognitive models and iterating over varying assumptions.
method Meta-amortized framework using a transformer to estimate parameters across multiple models.
result CogFormer accurately estimates parameters across different model families with minimal retraining.
A new SBI framework for trawl processes efficiently estimates parameters from large datasets.
problem Challenges in estimating parameters of complex stochastic processes.
method Telescoping ratio estimation, Chebyshev polynomial approximations, amortized posterior inference.
result Accurate and efficient inference for intractable stochastic processes, even with limited data.
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.
New method for Bayesian neural networks reduces inference difficulty.
problem Difficulty in sample-based inference for Bayesian neural networks.
method Embracing mode-connectedness to link overparameterization and sampling difficulty.
result Practical guidelines and deep ensemble approach for effective SBI.
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.
Pseudo-Likelihood Inference improves ABC for high-dimensional Bayesian inference.
problem Intractable likelihood in Bayesian system identification.
method PLI combines neural approximation with integral probability metrics and adaptive bandwidth.
result PLI outperforms SNPE on challenging tasks, especially with more data.
Scattering representations simplify SBI for images without extra compression.
problem Efficiently performing simulation-based inference on images with limited data.
method Use scattering representations for compression and learning, combined with spatial averaging and expressive density estimators.
result Scattering representations provide more information than traditional methods, without requiring additional simulations.
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.
Waldo method constructs valid confidence regions for simulator-based inference.
problem Constructing valid confidence regions for simulator-based inference with high-dimensional data.
method Reframes Wald test statistic and uses regression-based machinery for Neyman inversion.
result Waldo method produces conditionally valid and precise confidence regions.
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.
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.
RoPE framework calibrates misspecified simulators for reliable inference.
problem Misspecification compromises reliability of simulation-based inference.
method Data-driven calibration using optimal transport and a small calibration set.
result RoPE framework improves inference accuracy and uncertainty calibration.
Two synthetic likelihood methods learn EBM of likelihood from simulator data for SBI.
problem Conduct inference from experimental observations using high-fidelity simulators.
method Learn conditional EBM of likelihood using synthetic data conditioned on parameters.
result Learned likelihood combined with prior yields posterior estimate for sampling.
New method uses SBI to infer magnetorotational properties of isolated pulsars.
problem Constrain magnetorotational properties of isolated Galactic radio pulsars.
method Combines population synthesis with SBI to model neutron star birth and evolution.
result Inferred μlogB=13.10−0.10+0.08, σlogB=0.45−0.05+0.05 for lognormal distributions.