ABC Samplers detail methods for sampling from ABC approximations.
problem Sampling from ABC approximations to posterior distributions.
method Rejection/importance sampling, MCMC, sequential Monte Carlo.
result Various ABC sampling methods are detailed and compared.
Extends ABC methods to handle high-dimensional data.
problem Challenges in applying ABC methods to high-dimensional data.
method Extends ABC methods to higher dimensions with supporting examples.
result Improves applicability of ABC methods to complex data.
ABC-CDE tackles ABC challenges with high-dimensional data and limited simulations.
problem Challenges in ABC with high-dimensional data and costly simulations.
method ABC-CDE uses nonparametric conditional density estimation to address ABC challenges.
result ABC-CDE directly estimates and assesses the posterior based on an initial ABC sample.
Logitboost is an influential boosting algorithm for classification. In this paper, we develop robust logitboost to provide an explicit formulation of tree-split criterion for building weak learners (regression trees) for logitboost. This formulation leads to a numerically stable implementation of logitboost. We then pr…
Approximate Bayesian Computation (ABC) are likelihood-free Monte Carlo methods. ABC methods use a comparison between simulated data, using different parameters drew from a prior distribution, and observed data. This comparison process is based on computing a distance between the summary statistics from the simulated da…
A new ABC method simplifies Bayesian inference for complex models.
problem Computational difficulty in Bayesian inference for models without analytical likelihoods.
method Empirical likelihood ABC method that requires only summary statistics and simulation.
result The posterior obtained is consistent and performs well across various examples.
Proposes robust ABC method for outlier detection.
problem Outliers sensitivity in ABC methods.
method γ-divergence estimator with redescending property.
result Significantly higher robustness than existing methods.
The paper connects ABC to GBI, suggesting ABC as a robustification strategy.
problem Approximate Bayesian Computation struggles with tractability in complex simulators.
method Reinterpreting ABC as an implicitly defined error model and suggesting GBI.
result ABC can be seen as a robustification strategy for approximating Bayesian posteriors.
Improved ABC method using Gaussian processes for more efficient simulations and uncertainty quantification.
problem Efficiently simulate and quantify uncertainty in ABC methods.
method Batch-sequential Bayesian experimental design, numerical method for uncertainty quantification, improved GP modeling assumptions.
result Improved framework for ABC methods that quantifies uncertainty and parallelizes simulations.
New ABC method uses energy statistic for data comparison.
problem Handling intractable likelihood functions in Bayesian analysis.
method Importance Sampling ABC (IS-ABC) using two-sample energy statistic.
result Energy statistic-based ABC yields consistent pseudo-posterior distributions.
New ABC method uses neural emulators for inference in complex models.
problem Approximate Bayesian Computation (ABC) for models without tractable likelihoods.
method Probabilistic neural emulator networks to learn synthetic likelihoods, adaptive simulations.
result Accurate and efficient inference on high-dimensional problems.
ABC algorithms involve a large number of simulations from the model of interest, which can be very computationally costly. This paper summarises the lazy ABC algorithm of Prangle (2015), which reduces the computational demand by abandoning many unpromising simulations before completion. By using a random stopping decis…
A new ABC method uses variational approximations for efficient inference.
problem Computational challenges in Bayesian inference for complex models.
method Variational approximation for log-posterior, empirical likelihood for estimating expected log-likelihood, differential entropy estimation.
result Posterior consistency established for the proposed method.
A novel ABC method for high-dimensional inverse problems using generative modeling and subset simulation.
problem Solving inverse-problems with high-dimensional inputs and expensive forward mappings.
method Joint deep generative modeling, Approximate Bayesian Computation (ABC) with Subset Simulation, and likelihood-free inference.
result Our method delivers promising performance without prior knowledge of the forward or noise distributions.
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.
The paper studies the face angles of tetrahedra with a fixed base.
problem Determine the closure and boundary of the set of face angles of tetrahedra with a given base.
method Analyzes the set of tetrahedra with a given base and calculates the cosine of the angles between the faces.
result The closure and boundary of the set of face angles are determined.
Active learning method for ABC statistics selection reduces expert work and improves posterior estimates.
problem Handling intractable likelihood functions in models with domain knowledge.
method Active learning method for selecting summary statistics in ABC.
result Better posterior estimates than existing methods, especially with limited simulation budget.
A new method combines classification with population Monte Carlo for efficient ABC.
problem Inefficient particle proposals and subjectivity in ABC methods.
method Classification-PMC, blending adaptive proposals and classification.
result Classification-PMC outperforms state-of-the-art ABC methods in simulations.
ABC method uses machine learning for likelihood-free inference.
problem Statistical inference in simulator-based models with intractable likelihoods.
method Direct comparison of empirical distributions via KL divergence estimator and contrastive learning.
result Asymptotic normality of ABC posterior distributions with properly scaled exponential kernel.
Explains ABC methods with examples.
problem Statistical inference for complex models.
method Approximate Bayesian Computation (ABC).
result Comprehensive overview of ABC methods.
Approximate Bayesian computation (ABC) is a powerful and elegant framework for performing inference in simulation-based models. However, due to the difficulty in scaling likelihood estimates, ABC remains useful for relatively low-dimensional problems. We introduce Hamiltonian ABC (HABC), a set of likelihood-free algori…
Many models of interest in the natural and social sciences have no closed-form likelihood function, which means that they cannot be treated using the usual techniques of statistical inference. In the case where such models can be efficiently simulated, Bayesian inference is still possible thanks to the Approximate Baye…
Approximate Bayesian computation (ABC) methods are used to approximate posterior distributions using simulation rather than likelihood calculations. We introduce Gaussian process (GP) accelerated ABC, which we show can significantly reduce the number of simulations required. As computational resource is usually the mai…
Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulation experiments indicate, however, that model posterior probabilities may be poorly evaluated by standard ABC techniques. We propose a novel…
New linking numbers link complex cycles to Calabi-Yau 3-folds.
problem Understanding complex analytic cycles and their Massey products.
method Relating ABC Massey products to holomorphic linking numbers.
result Constructed a family of Calabi-Yau 3-folds with non-vanishing Massey products.
New method uses neural exponential families for likelihood-free inference.
problem Bayesian Likelihood-Free Inference with intractable likelihood.
method Score Matching neural conditional exponential families for approximate likelihood.
result State-of-the-art performance in posterior sampling for intractable likelihood models.
A new ABC technique using Sliced-Wasserstein distance improves inference quality.
problem Intractable likelihood in generative models leads to loss of information in summary statistics.
method Proposes Sliced-Wasserstein ABC, a new ABC technique based on the Sliced-Wasserstein distance.
result Derives theoretical consistency results and demonstrates improved performance on synthetic and image denoising tasks.
Abc-boost is a new line of boosting algorithms for multi-class classification, by utilizing the commonly used sum-to-zero constraint. To implement abc-boost, a base class must be identified at each boosting step. Prior studies used a very expensive procedure based on exhaustive search for determining the base class at …
qABC accelerates ABC for cosmological redshift distributions.
problem Slow convergence rate in Approximate Bayesian Computation (ABC).
method qABC uses Quantile Regression to model quantiles of distance measures as a function of input parameters, accelerating ABC convergence.
result qABC converges to same posterior as basic ABC but uses only 20% of simulations, achieving a fivefold gain in execution time.
Proposes a novel method for estimating parameters in simulator-based models with intractable likelihood.
problem Parameter estimation for simulator-based models with unfeasible likelihood calculations.
method Recursive application of kernel ABC and kernel herding to observed data.
result The method converges to the true parameter as recursion proceeds, outperforming existing approaches in numerical experiments.
Develops a privacy-preserving ABC method using SVT.
problem Privacy in approximate Bayesian computation.
method Integrates Sparse Vector Technique (SVT) into ABC for differential privacy.
result Produces differentially private posterior samples with minimal modification.
This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100036). Approximate Bayesian computation (ABC) has grown into a standard methodology that manages Bayesian inference for models associated with intractable likelihood functions. Most ABC i…
ABC method improves subseasonal weather forecasting by 60-90%.
problem Improving subseasonal temperature and precipitation forecasting accuracy.
method Combines dynamical forecasts with machine learning-based bias correction.
result Significant improvement in temperature and precipitation forecasting skills.
ABC uses autoencoders for better anomaly detection.
problem Detecting both known and unknown anomalies accurately.
method Probabilistic binary classifier using Autoencoder for normal data reconstruction.
result ABC outperforms existing methods in anomaly detection.
AgABC improves ABC algorithm by balancing exploration and exploitation.
problem Balancing global and local search abilities in ABC algorithm.
method Divide population into groups and assign different search strategies to members.
result Proposed AgABC algorithm outperforms other algorithms in accuracy and stability.
Proposes ABC method for discrete data, improving likelihood-free inference.
problem Discrete data likelihood-free inference problems.
method Population-based MCMC ABC framework with a new Markov kernel inspired by Differential Evolution.
result High potential and superiority of the new Markov kernel demonstrated.
This document is an invited chapter covering the specificities of ABC model choice, intended for the incoming Handbook of ABC by Sisson, Fan, and Beaumont (2017). Beyond exposing the potential pitfalls of ABC based posterior probabilities, the review emphasizes mostly the solution proposed by Pudlo et al. (2016) on the…
The intention of this paper is to estimate a Bayesian distribution-free chain ladder (DFCL) model using approximate Bayesian computation (ABC) methodology. We demonstrate how to estimate quantities of interest in claims reserving and compare the estimates to those obtained from classical and credibility approaches. In …
Complicated generative models often result in a situation where computing the likelihood of observed data is intractable, while simulating from the conditional density given a parameter value is relatively easy. Approximate Bayesian Computation (ABC) is a paradigm that enables simulation-based posterior inference in su…
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.
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.
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.
New ABC method improves Bézier simplex fitting for noisy data.
problem Overfitting in Bézier simplex fitting when sample points are not on the Pareto set.
method Extended Bézier simplex model to a probabilistic one and proposed a new learning algorithm based on approximate Bayesian computation (ABC) with Wasserstein distance.
result The new algorithm converges on a finite sample and outperforms deterministic methods on noisy instances.
This paper introduces a simple, general framework for likelihood-free Bayesian reinforcement learning, through Approximate Bayesian Computation (ABC). The main advantage is that we only require a prior distribution on a class of simulators (generative models). This is useful in domains where an analytical probabilistic…
Scientists often express their understanding of the world through a computationally demanding simulation program. Analyzing the posterior distribution of the parameters given observations (the inverse problem) can be extremely challenging. The Approximate Bayesian Computation (ABC) framework is the standard statistical…
Approximate Bayesian computation (ABC) is a method for Bayesian inference when the likelihood is unavailable but simulating from the model is possible. However, many ABC algorithms require a large number of simulations, which can be costly. To reduce the computational cost, Bayesian optimisation (BO) and surrogate mode…
New methods improve statistical accuracy of complex models without high computational cost.
problem Improving statistical accuracy of complex models without high computational cost.
method Neural posterior and likelihood estimation (NPE and NLE) methods.
result NPE and NLE methods have similar theoretical guarantees to ABC and BSL, but achieve accuracy at a reduced computational cost.
Survey of Monte Carlo methods for noisy, costly densities in reinforcement learning and ABC.
problem Dealing with intractable, costly, and noisy densities in real-world scenarios.
method Classification and description of Monte Carlo methodologies using surrogate models.
result Unified scheme and numerical comparisons of different methodologies.