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Efficient Bayesian inference for finite element model updating with surrogate modeling techniques
Bayesian finite element model updating has become an important tool for structural health monitoring. However, it takes a large amount of...
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Measurement issues in causal inference
Research in the social and behavioral sciences relies on a wide range of experimental and quasi-experimental designs to estimate the causal effects...
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Approximate inference for longitudinal mechanistic HIV contact network
Network models are increasingly used to study infectious disease spread. Exponential Random Graph models have a history in this area, with scalable...
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Invited Commentary: Bayesian Inference with Multiple Tests
Dr. Leonhard presents a comprehensive and insightful critique of the existing malingering research literature and its implications for...
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Dynamical flexible inference of nonlinear latent factors and structures in neural population activity
Modelling the spatiotemporal dynamics in the activity of neural populations while also enabling their flexible inference is hindered by the...
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Generative models for sequential dynamics in active inference
A central theme of theoretical neurobiology is that most of our cognitive operations require processing of discrete sequences of items. This...
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Monotonic Inference with Unscoped Episodic Logical Forms: From Principles to System
We describe the foundations and the systematization of natural logic-like monotonic inference using unscoped episodic logical forms (ULFs) that as...
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Evaluation of single-sample network inference methods for precision oncology
A major challenge in precision oncology is to detect targetable cancer vulnerabilities in individual patients. Modeling high-throughput omics data in...
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Sparse inference and active learning of stochastic differential equations from data
Automatic machine learning of empirical models from experimental data has recently become possible as a result of increased availability of...
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A Survey on Collaborative DNN Inference for Edge Intelligence
With the vigorous development of artificial intelligence (AI), intelligence applications based on deep neural networks (DNNs) have changed people’s...
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Conspiracy beliefs and perceptual inference in times of political uncertainty
Sociopolitical crises causing uncertainty have accumulated in recent years, providing fertile ground for the emergence of conspiracy ideations....
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Harnessing deep learning for population genetic inference
In population genetics, the emergence of large-scale genomic data for various species and populations has provided new opportunities to understand...
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Inference in conditioned dynamics through causality restoration
Estimating observables from conditioned dynamics is typically computationally hard. While obtaining independent samples efficiently from...
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Variational inference: uncertainty quantification in additive models
Markov chain Monte Carlo (MCMC)-based simulation approaches are by far the most common method in Bayesian inference to access the posterior...
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Stoic Sign-Inference and Their Lore of Fate
The Stoics are traditionally regarded as the founders of propositional logic. However, this is not entirely correct. They developed a theory of...
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Attention-based variable-size feature compression module for edge inference
Artificial intelligence has made significant breakthroughs in many fields, especially with the broad deployment of edge devices, which provides...
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Propensity score methods for causal inference and generalization
As evidence from evaluation and experimental studies continue to influence decision and policymaking, applied researchers and practitioners require...
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Likelihood-free inference in state-space models with unknown dynamics
Likelihood-free inference (LFI) has been successfully applied to state-space models, where the likelihood of observations is not available but...
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Alternate inference-decision reinforcement learning with generative adversarial inferring for bridge bidding
Contract bridge is a competitive-cooperative multiplayer game. In the bidding phase, the decision-making process is complex, given the extensive...
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Multi-scale adaptive networks for efficient inference
The success of deep neural networks has been impressive in many areas. However, the increase in model performance is usually accompanied by an...
