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.
In this paper we study simulation based optimization algorithms for solving discrete time optimal stopping problems. This type of algorithms became popular among practioneers working in the area of quantitative finance. Using large deviation theory for the increments of empirical processes, we derive optimal convergenc…
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.
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.
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.
Two simulation-based methods improve optimal sampling design in systems biology.
problem Optimal selection of sampling points for accurate parameter estimation in dynamical systems.
method E-optimal-ranking (EOR) and LSTM neural network-based methods.
result Simulation studies show the proposed methods outperform random selection and classical E-optimal design.
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.
SBBO optimizes complex spaces using sampling-based models.
problem Optimizing complex spaces with discrete variables.
method Simulation Based Bayesian Optimization (SBBO) using sampling-based surrogate models.
result Empirical effectiveness of SBBO in combinatorial optimization.
Paper introduces a new sampler for simulation-based inference using Gromov-Monge distance.
problem Simulation-based inference for multi-dimensional probability distributions.
method Proposes Reversible Gromov-Monge (RGM) distance and sampler for alignment and inference.
result RGM sampler can estimate optimal alignments and push measures between spaces.
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.
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.
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.
A new method for adaptive experiments improves inference.
problem Current inference methods for adaptive experiments are weak and asymptotic.
method Simulation-based inference using optimistic simulations.
result Our method achieves better coverage and narrower intervals.
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.
Our paper deals with inferring simulator-based statistical models given some observed data. A simulator-based model is a parametrized mechanism which specifies how data are generated. It is thus also referred to as generative model. We assume that only a finite number of parameters are of interest and allow the generat…
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.
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.
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.
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.
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.
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.
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 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.
The optimal selection of experimental conditions is essential to maximizing the value of data for inference and prediction, particularly in situations where experiments are time-consuming and expensive to conduct. We propose a general mathematical framework and an algorithmic approach for optimal experimental design wi…
New method optimizes sensor placement for stochastic systems efficiently.
problem Optimizing sensor placements for black-box stochastic systems with computational constraints.
method Trains a joint energy-based model on simulation data to learn parameter and solution distributions, allowing efficient sensor placement.
result Demonstrates lower computational cost and more informative sensor locations compared to conventional approaches.
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.
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.
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.
Paper uses learned summary statistics for Bayesian inference with difficult likelihood functions.
problem Difficult to obtain exact likelihood function for observation data and simulation model.
method Simulation-based inference with learned summary statistics, using Cressie-Read discrepancy criterion.
result Effective inference performed over selected sample sets of observation data.
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.
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.
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.
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.
SSNL improves simulation-based inference for high-dimensional data.
problem Performance degradation in neural likelihood estimation for high-dimensional data.
method Surjective Sequential Neural Likelihood (SSNL) using surjective normalizing flow models.
result SSNL avoids manual crafting of summary statistics and outperforms state-of-the-art methods.
New method improves robustness and efficiency of inference from complex models.
problem Inability of existing methods to handle outliers in simulator-based models.
method Generalised Bayesian inference with neural approximation of weighted score-matching loss.
result Provable robustness to outliers and computational efficiency.
A method for profiling systematic uncertainties in SBI using Factorizable Normalizing Flows.
problem Computational cost and limited applicability of current SBI methods for realistic analyses.
method Simulation-Based Inference with Factorizable Normalizing Flows to model systematic variations.
result Efficient profiling of nuisance parameters and multivariate DoI in complex analyses.
Proposes EDESH-SA for better inventory management under uncertainty.
problem Inventory management under uncertainty.
method Ensemble Differential Evolution with simulation-based hybridization and self-adaptation.
result Improves financial performance and optimizes search spaces.
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.
Simulation-based inference speeds up gravitational wave data analysis.
problem High-dimensional parameter spaces and complex noise in gravitational wave data.
method Simulation-based inference methods using machine learning techniques.
result Simulation-based inference methods improve speed over traditional methods.
New method improves simulation-based inference by avoiding model misspecification.
problem Inefficient parameter estimation for models with intractable likelihoods.
method Proposes a robust SNL method with additional adjustment parameters.
result Demonstrates more accurate point estimates and uncertainty quantification.
Study uses simulation-based inference to decode brain activity from synthetic stimuli.
problem Reversing the process of brain activity emulation to recover stimuli or their properties.
method Pairing brain emulator with LLMs to learn a probabilistic mapping from brain maps to stimulus parameters.
result LLMs can serve as controllable stimulus generators and parameters can be recovered from brain maps.
New method detects and measures malicious users in recommendation algorithms.
problem Identifying and quantifying malicious user activity in recommendation systems.
method Probabilistic programming for a disentangled model of malicious and regular user behavior.
result Simulation-based measure for quantifying malicious user effects.
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 machine learning methods for inference from simulated data.
problem Modeling score and likelihood ratio functions from sampled data.
method InferoStatic Networks (ISN), Kernel Score Estimation (KSE), Kernel Likelihood Ratio Estimation (KLRE).
result Improved inference methods for complex models.
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.
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.
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.
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.