arXiv Machine Learning

Adapting Neural Posterior Estimators for Simulation-Based Inference in Agent-Based Models

The paper presents a framework called SNPE‑TTT for adapting pre‑trained neural posterior estimators, specifically conditional normalizing flows, to new inference tasks in agent‑based models (ABMs). It explores four adaptation strategies—full‑parameter adaptation, low‑rank adaptation, and two gradient‑based update spaces—and evaluates them on economic simulation models with both tractable and intractable likelihoods. Results show that the framework can improve posterior recovery with a limited number of target simulations, though performance varies by task and adaptation method.

Hugging Face Trending Papers

Jun 28

Persona-Trained Monte Carlo: Estimating Market-Outcome Distributions via Swarms of Persona-Conditioned Neural Policy Bots in a Limit Order Book

We propose Persona-Trained Monte Carlo (PTMC), a method for estimating distributions of market-outcome statistics by repeatedly simulating limit-order-book interaction among swarms of persona-conditioned neural-policy trading bots. Each run instantiates many bots sharing one trained policy network but conditioned on heterogeneous, individually sampled persona parameters drawn from a learned trader-heterogeneity distribution; the bots interact in a continuous double auction, and the resulting price path is one Monte Carlo sample.

llms agents reinforcement-learning

Hugging Face Trending Papers

Sep 21

Prior-Amortized In-Context Bayesian Inference for Generalized Linear Mixed-Effects Models

Hierarchical data is ubiquitous in the empirical sciences and is most commonly analyzed with generalized linear mixed-effects models (GLMMs). Bayesian inference for GLMMs yields calibrated uncertainty...

llms benchmarks

arXiv Machine Learning

Jun 30

Persona-Trained Monte Carlo: Estimating Market-Outcome Distributions via Swarms of Persona-Conditioned Neural Policy Bots in a Limit Order Book

arXiv:2606. 29556v1 Announce Type: new Abstract: We propose Persona-Trained Monte Carlo (PTMC), a method for estimating distributions of market-outcome statistics by repeatedly simulating limit-order-book interaction among swarms of persona-conditioned neural-policy trading bots.

By Salavat Ishbulatov

llms agents reinforcement-learning

arXiv Machine Learning

Aug 17

Neural Network-Based Parameter Estimation of a Labour Market Agent-Based Model

arXiv:2602. 15572v3 Announce Type: replace Abstract: Agent-based modelling (ABM) is a widespread approach to simulate complex systems.

By M Lopes Alves, Joel Dyer, Doyne Farmer, Michael Wooldridge, Anisoara Calinescu

agents benchmarks

arXiv Machine Learning

Sep 22

Prior-Amortized In-Context Bayesian Inference for Generalized Linear Mixed-Effects Models

arXiv:2609.24422v1 Announce Type: new Abstract: Hierarchical data is ubiquitous in the empirical sciences and is most commonly analyzed with generalized linear mixed-effects models (GLMMs). Bayesian...

By Alex Kipnis, Marcel Binz, Eric Schulz

llms benchmarks

arXiv Machine Learning

Sep 30

High-Dimensional Simulation-Based Inference in Latent Spaces

arXiv:2609.37381v1 Announce Type: new Abstract: Neural simulation-based inference (SBI) has been widely successful in inferring a relatively small number of interpretable parameters from potentially...

By Lars K\"uhmichel, Stefan T. Radev, Bhanu Prasanna Koppolu, Masoumeh Davoudi, Jerry M. Huang, Paul-Christian B\"urkner

diffusion

arXiv Machine Learning

Jul 17

Neural Architectures for Amortized Bayesian Inference: Statistical Foundations and Empirical Assessments

arXiv:2601. 07944v2 Announce Type: replace-cross Abstract: Since the turn of the century, approximate Bayesian inference has steadily evolved as new computational techniques have been incorporated to handle increasingly complex, large-scale predictive problems.

By Roy Shivam Ram Shreshtth, Arnab Hazra, Gourab Mukherjee

llms efficiency

arXiv Machine Learning

Jun 15

Minimum Distance Summaries for Robust Neural Posterior Estimation

arXiv:2602. 09161v2 Announce Type: replace-cross Abstract: Simulation-based inference (SBI) enables amortized Bayesian inference by first training a neural posterior estimator (NPE) on prior-simulator pairs, typically through low-dimensional summary statistics, which can then be cheaply reused for fast inference by querying it on new test observations.

By Sherman Khoo, Dennis Prangle, Song Liu, Mark Beaumont

arXiv Machine Learning

Sep 1

Amortizing intractable inference in large language models

arXiv:2310.04363v3 Announce Type: replace Abstract: Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions. This limits t...

By Edward J. Hu, Moksh Jain, Eric Elmoznino, Younesse Kaddar, Guillaume Lajoie, Yoshua Bengio, Esmeralda S. Whitammer

llms agents reinforcement-learning fine-tuning

arXiv AI

Jun 29

The Simulacrum: Decision-Theoretic Pretraining for Near-Optimal Time-Series Forecasting and Inference

arXiv:2606. 27711v1 Announce Type: cross Abstract: We introduce a neural network-based framework for learning time series estimators through a process we term decision-theoretic pretraining.

By Pablo Montero-Manso, Marcel Scharth

benchmarks safety

arXiv Machine Learning

Oct 15

Semi-Amortized Bayesian Inference with Reusable Local Posteriors

arXiv:2610.18945v1 Announce Type: cross Abstract: Amortized Bayesian inference uses simulations to perform Bayesian estimation when likelihoods are expensive or analytically unavailable. However, lea...

By Niek Stevenson, Paul-Christian B\"urkner, Andrew Heathcote, Stefan T. Radev

arXiv AI

Jul 1

Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation

arXiv:2606. 31184v1 Announce Type: cross Abstract: Adaptive experiments for average treatment effects (ATE) require randomized allocations balancing valid inference with statistical efficiency.

By Jiachun Li, David Simchi-Levi

llms

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