approximate bayesian computation
Multilevel neural simulation-based inference
Neural simulation-based inference (SBI) is a popular set of methods for Bayesian inference when models are only available in the form of a simulator. These methods are widely used in the sciences and engineering, where writing down a likelihood can be significantly more challenging than constructing a simulator. However, the performance of neural SBI can suffer when simulators are computationally expensive, thereby limiting the number of simulations that can be performed. In this paper, we propose a novel approach to neural SBI which leverages multilevel Monte Carlo techniques for settings where several simulators of varying cost and fidelity are available. We demonstrate through both theoretical analysis and extensive experiments that our method can significantly enhance the accuracy of SBI methods given a fixed computational budget.
Fast ฮต-free Inference of Simulation Models with Bayesian Conditional Density Estimation
George Papamakarios, Iain Murray
Neural Information Processing Systems http://nips.cc/
Predictive Approximate Bayesian Computation via Saddle Points
Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He
Neural Information Processing Systems http://nips.cc/
Flexible statistical inference for mechanistic models of neural dynamics
Jan-Matthis Lueckmann, Pedro J. Goncalves, Giacomo Bassetto, Kaan รcal, Marcel Nonnenmacher, Jakob H. Macke
Neural Information Processing Systems http://nips.cc/
Predictive Approximate Bayesian Computation via Saddle Points
Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He
Approximate Bayesian computation (ABC) is an important methodology for Bayesian inference when the likelihood function is intractable.
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference
Neural Information Processing Systems http://nips.cc/
Uncertainty Quantification of Large Language Models using Approximate Bayesian Computation
Sharma, Mridul, Patel, Adeetya, Souza, Zaneta D', Rahimi, Samira Abbasgholizadeh, Reddy, Siva, Madathil, Sreenath
Despite their widespread applications, Large Language Models (LLMs) often struggle to express uncertainty, posing a challenge for reliable deployment in high stakes and safety critical domains like clinical diagnostics. Existing standard baseline methods such as model logits and elicited probabilities produce overconfident and poorly calibrated estimates. In this work, we propose Approximate Bayesian Computation (ABC), a likelihood-free Bayesian inference, based approach that treats LLMs as a stochastic simulator to infer posterior distributions over predictive probabilities. We evaluate our ABC approach on two clinically relevant benchmarks: a synthetic oral lesion diagnosis dataset and the publicly available GretelAI symptom-to-diagnosis dataset. Compared to standard baselines, our approach improves accuracy by up to 46.9\%, reduces Brier scores by 74.4\%, and enhances calibration as measured by Expected Calibration Error (ECE) and predictive entropy.