Bayesian analysis reveals asymmetry in financial data.
problem Quantifying asymmetry in financial time series data.
method Bayesian approach, t-Test generalization, two data distribution models, sensitivity analysis.
result Statistical significance of gain/loss asymmetry amounts.
We present two alternative ways to apply PAC-Bayesian analysis to sequences of dependent random variables. The first is based on a new lemma that enables to bound expectations of convex functions of certain dependent random variables by expectations of the same functions of independent Bernoulli random variables. This …
BOAH optimizes expensive hyperparameter searches quickly.
problem Expensive hyperparameter optimization for neural networks.
method Multi-fidelity Bayesian optimization and HyperBand integration.
result Efficient optimization of complex design spaces.
Develops BPDS for better financial portfolio decisions.
problem Model uncertainty in financial time series forecasting.
method Bayesian dynamic modelling and predictive decision synthesis.
result Improved predictive and decision outcomes compared to traditional Bayesian analysis.
Enhances FA framework for heterogeneous data with feature selection and semi-supervised learning.
problem Feature extraction and latent representation of heterogeneous data.
method Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis (SSHIBA) framework.
result SSHIBA outperforms state-of-the-art algorithms and shows interpretability gains.
New method combines FMEA and Bayesian Network for root cause analysis in lithium-ion battery production.
problem Complex cause-effect relationships in lithium-ion battery production.
method Combining FMEA with Bayesian Network to detect and resolve inconsistencies.
result Holistic method builds large-scale cross-process Bayesian Failure Network for root cause analysis.
Bayesian adaptive PCE method improves surrogate modeling and sensitivity analysis.
problem Lack of fully Bayesian PCE methods in statistics.
method Developed a novel fully Bayesian adaptive PCE method with R implementation.
result Bayesian adaptive PCE provides competitive performance for various UQ tasks.
Proposes OBS, a method to adaptively combine Bayesian models online.
problem Learning optimal combinations of Bayesian models in online learning.
method Empirical Bayes lens, Online Bayesian Stacking (OBS).
result Establishes a novel connection between OBS and portfolio selection.
Empirical analysis serves as an important complement to theoretical analysis for studying practical Bayesian optimization. Often empirical insights expose strengths and weaknesses inaccessible to theoretical analysis. We define two metrics for comparing the performance of Bayesian optimization methods and propose a ran…
Bayesian neural networks use temperature adjustments to improve predictive performance.
problem Lack of theoretical generalization guarantees for Bayesian neural networks.
method Temperature adjustments to balance likelihood and prior regularization.
result Improved predictive performance through temperature adjustments.
GBEST model improves survival analysis for small datasets.
problem Challenges in survival analysis, especially with small data.
method Bayesian bootstrap and Beta Stacy bootstrap methods integrated into bagging tree models.
result GBEST model outperforms classical survival models in predictive performance and stability.
New analysis shows Bayesian model averaging is suboptimal under misspecification.
problem Generalization performance of Bayesian model averaging under model misspecification.
method Novel second-order PAC-Bayes bounds to analyze generalization performance.
result New Bayesian-like algorithms with better generalization performance.
Attack graphs are a powerful tool for security risk assessment by analysing network vulnerabilities and the paths attackers can use to compromise network resources. The uncertainty about the attacker's behaviour makes Bayesian networks suitable to model attack graphs to perform static and dynamic analysis. Previous app…
We derive a novel sensitivity analysis of input variables for predictive epistemic and aleatoric uncertainty. We use Bayesian neural networks with latent variables as a model class and illustrate the usefulness of our sensitivity analysis on real-world datasets. Our method increases the interpretability of complex blac…
The study uses statistical methods to analyze nuclear mass models.
problem Understanding the information content of nuclear masses from models.
method Bayesian calibration, Bayesian model averaging, chi-square correlation analysis, principal component analysis.
result A dramatic parameter reduction can be achieved in both 4-parameter and 14-parameter models.
We propose an extensive analysis of the behavior of majority votes in binary classification. In particular, we introduce a risk bound for majority votes, called the C-bound, that takes into account the average quality of the voters and their average disagreement. We also propose an extensive PAC-Bayesian analysis that …
Bayesian approach helps detect adversarial examples in neural networks.
problem Detecting adversarial examples in deep neural networks.
method Adversarial spheres and approximate Bayesian inference for a linear model.
result Bayesian methods provide better predictions of adversarial points.
NeuralSurv models survival analysis with Bayesian uncertainty.
problem Capturing time-varying risk relationships in survival analysis.
method Two-stage data-augmentation scheme, mean-field variational algorithm, coordinate-ascent updates, locally linearized Bayesian neural network.
result Delivers superior calibration compared to state-of-the-art models.
The paper compares Bayesian trees, Cox models, and random forests for breast cancer survival data.
problem Modeling survival data with nonlinear and additive effects.
method Bayesian Additive Regression Trees, Cox proportional hazards, and Random Survival Forests.
result Bayesian trees outperform other models in terms of bias and prediction accuracy.
VarFA efficiently estimates student skill levels with uncertainty for adaptive testing.
problem Efficiently estimating student skill levels with uncertainty for adaptive testing.
method VarFA uses variational inference to extend factor analysis models for educational data.
result VarFA efficiently handles large datasets and produces uncertainty estimates.
Sharp bounds for Dirichlet sums lead to improved Bayesian algorithm analysis.
problem Improving Bayesian algorithm performance through precise deviation bounds.
method Novel integral representation of Dirichlet sum density, Gaussian approximation, complex analysis.
result Significantly sharpened regret bounds for Multinomial Thompson Sampling.
Unified Bayesian framework for PTA data analysis tackles hierarchical model issues.
problem Hierarchical Bayesian modeling challenges in PTA data analysis.
method Reparameterization strategy using Normalizing Flows (NFs) and i-nessai nested sampler.
result Improved statistical robustness and computational efficiency in PTA analysis.
New method explains sensitivity of test data uncertainty in Bayesian inference.
problem Widespread belief that test data similarity reduces epistemic uncertainty.
method Information-theoretic decomposition of predictive uncertainty.
result Defines sensitivity using information-theoretic quantities.
Two batch Bayesian optimization algorithms with regret guarantees.
problem Efficiently optimizing multiple objectives in batch feedback settings.
method Gaussian process upper confidence bound and Thompson sampling approaches.
result Frequentist regret guarantees and numerical results.
Qualitative analysis of MC dropout for NN model uncertainty.
problem Measuring uncertainty in neural network models.
method Mathematical formulation of Monte Carlo dropout and its benefits/costs in NN models.
result Potential benefits and associated costs of using MC dropout in NN models.
Proposes a TS approach for Bayesian optimization with preferential feedback.
problem Optimizing with preference feedback in complex applications.
method Uses Thompson Sampling with a dueling kernel and anchor invariance.
result Performance matches standard TS for scalar feedback in finite time.
BSD is a Bayesian framework for analyzing neural spectral data.
problem Challenges in statistical analysis and group-level comparisons of neural power spectra.
method Bayesian Spectral Decomposition (BSD) for parametric models of neural spectra.
result BSD outperforms existing methods in model selection and parameter estimation.
We develop a coherent framework for integrative simultaneous analysis of the exploration-exploitation and model order selection trade-offs. We improve over our preceding results on the same subject (Seldin et al., 2011) by combining PAC-Bayesian analysis with Bernstein-type inequality for martingales. Such a combinatio…
The paper tackles mean-variance analysis in Bayesian optimization under uncertainty.
problem Optimizing decisions in uncertain environments considering trade-offs between average and variance of risk.
method Developed bounds for mean and variance risk measures in Gaussian Process models and proposed AL algorithms for multi-task, multi-objective, and constrained optimization scenarios.
result Proposed AL algorithms effectively address the mean-variance trade-off in uncertain optimization scenarios.
Study analyzes EU in variational inference for Bayesian deep learning.
problem Analyzing epistemic uncertainty in variational inference for Bayesian deep learning.
method Theoretical analysis and derivation of relations between generalization error and EU measurements. Proposing a new objective function for VI.
result Proposed new objective function significantly improves EU evaluation over existing VI methods.
This paper offers a simple method for Bayesian regression with unknown transformations.
problem Joint inference of unknown transformations and model parameters in Bayesian regression is computationally inefficient and cumbersome.
method The paper introduces a Bayesian nonparametric model via the Bayesian bootstrap to directly target the posterior distribution of the transformation.
result The approach delivers joint posterior consistency and efficient Monte Carlo inference for the transformation and all parameters.
Blang simplifies Bayesian analysis for non-standard data types.
problem Bayesian inference for non-standard data structures.
method Bayesian declarative language, distribution continua, sequential Monte Carlo, non-reversible MCMC.
result Bayesian analysis on arbitrary data types is feasible and efficient.
Bayesian analysis of financial time series using R-INLA.
problem Analyzing interdependencies between stock volatility measures.
method Flexible level correlated model (LCM) with INLA approximation.
result Fast approximate Bayesian modeling of positive-valued time series.
Paper examines stability of Bayesian posterior measures using integral probability metrics.
problem Stability of Bayesian inference in large-scale inverse problems.
method New families of integral probability metrics for likelihood and prior perturbations.
result Constructs new stability results for Bayesian posterior measures.
Bayesian sensitivity analysis accounts for model uncertainty.
problem Assessing variable importance in nonlinear models.
method Differentiating Rényi divergence of predictive distribution.
result Accurate and reliable identification of important variables and interactions.
Paper proposes a new method to attack Bayesian forecasting models.
problem Lack of research on adversarial attacks against time series forecasting systems.
method Decision analysis based attacking strategy for Bayesian forecasting models.
result Demonstrates the vulnerability of Bayesian forecasting models to adversarial attacks.
Deep model learns complex latent codes without assuming factor structure.
problem Learning latent codes with complex, non-factorial distributions.
method Deep generative factor analysis with beta process prior and stochastic EM algorithm.
result Preliminary results show model can approximate complex distributions.
We harness the power of Bayesian emulation techniques, designed to aid the analysis of complex computer models, to examine the structure of complex Bayesian analyses themselves. These techniques facilitate robust Bayesian analyses and/or sensitivity analyses of complex problems, and hence allow global exploration of th…
Bayesian framework for comparing trading algorithms using cost analysis.
problem Comparing trading algorithms using cost analysis.
method Bayesian framework, hierarchical models, standardized benchmarks, fat tails, skewness, heteroscedasticity.
result Effective calculation of trading benchmarks with limited data.
Bayesian SSI improves modal parameter uncertainty in operational systems.
problem Uncertainty in modal parameters due to stochastic operational systems and lack of forcing information.
method Proposes a Bayesian stochastic subspace identification (SSI) algorithm with a hierarchical probabilistic model and two inference schemes (Markov Chain Monte Carlo and variational Bayes).
result Posterior distributions over modal properties are obtained, showing lower variance for mean values coinciding with natural frequencies.
Improves model predictability by mixing forecasts and orthogonalizing models.
problem Redundant models contaminate model space and degrade predictive performance.
method Principal Component Analysis for model orthogonalization in Bayesian forecast mixing.
result Better prediction accuracy and excellent uncertainty quantification.
This paper introduces Bayes Hilbert spaces for efficient posterior approximation.
problem Efficient posterior approximation in Bayesian models for large datasets.
method Develops Bayes Hilbert spaces for posterior approximation and connects them to Bayesian coresets and kernel-based distances.
result Bayes Hilbert spaces provide a novel framework for posterior approximation that is computationally efficient.
Develops a new PAC-Bayesian bound for evaluating randomness in predictors.
problem Lack of a PAC-Bayesian bound for deterministic predictors and continuous loss functions.
method PAC-Bayesian transportation bound unifying PAC-Bayesian and chaining methods.
result First PAC-Bayesian bound relating risks of any two predictors by their distance.
MAntRA combines machine learning and Bayesian methods for time-dependent reliability analysis of unknown systems.
problem Time-dependent reliability analysis of systems with unknown governing physics.
method Combines machine learning, Bayesian statistics, and stochastic integration to discover and analyze SDEs from data.
result Demonstrates the effectiveness of MAntRA on three numerical examples, indicating its potential for in-situ and heritage structure analysis.
PAC-Bayesian bounds estimate adversarial robustness.
problem Estimating robustness to imperceptible input perturbations.
method PAC-Bayesian framework for averaging over hypotheses.
result General bounds valid for any type of adversarial attacks.
New analysis proves consistency for adaptive Bayesian quadrature methods.
problem No theoretical guarantees for adaptive Bayesian quadrature methods.
method Introduces weak adaptivity and proves consistency for a broad class of adaptive Bayesian quadrature rules.
result Proves consistency and derives non-tight but informative convergence rates for adaptive Bayesian quadrature methods.
Bayesian model improves cure fraction estimation in survival analysis.
problem Estimating cure fraction in survival analysis.
method Hierarchical Bayesian framework for semiparametric mixture cure model.
result Improved accuracy in capturing unobserved heterogeneity.
Developed a flexible Bayesian g-formula for causal survival analysis with time-dependent confounding.
problem Estimating causal survival curves in longitudinal observational studies with time-varying treatments and confounding.
method Incorporated Bayesian Additive Regression Trees (BART) into the g-formula to model time-evolving generative components and mitigate bias due to model misspecification.
result Demonstrated improved empirical performance and practical utility of the proposed method through simulations and real-world data analysis.