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Every paper Pith has read. Search by title, abstract, or pith.
377 papers in stat.CO · page 4
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New algorithm fits linear models in p-adics under digit noise
$p$-adic Linear Regression for Random Sampling with Digitwise Noise
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Multi-object posterior sampled via explicit Bernoulli conditionals
Multi-Object Posterior Computation via Gibbs Sampling
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This paper studies mixtures of Markov kernels that blend a baseline sampler with a Gibbs…
On additive averaging kernels for finite Markov chains
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Small method combinations match top performers in 90-95% of cases
An Empirical Comparison of Methods for Quantifying the Similarity of Numeric Datasets
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Seven-parameter model estimates dependent stress-strength reliability
Reliability estimation in dependent stress-strength model with Clayton copula and modified Weibull margins
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Sobolev CLR penalties align functional data without derivative noise
Sobolev-Regularized Objective Functions for Robust Pairwise Alignment of Functional Data
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NetworkNet estimates nodal expansiveness and popularity
NetworkNet: A Deep Neural Network Approach for Random Networks with Sparse Nodal Attributes and Complex Nodal Heterogeneity
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Friedman-Rafsky test recommended for two categorical datasets
An Empirical Comparison of Methods for Quantifying the Similarity of Categorical Datasets
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iglm package enables regression under interference in networks
R Package iglm: Regression under Interference in Connected Populations
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Fixed uniforms turn into exact Beta(a,1-a) samples
Extended One-Liners for the Beta, Gamma, and Dirichlet Distributions with Shape Parameters Below One
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Symmetric polynomials yield closed-form optimal FWER tests
Optimal multiple testing under family-wise error control: elementary symmetric polynomials and a scalable algorithm
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Neural nets replace linear predictors in mixed-effects models
Neural Generalized Mixed-Effects Models
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R package blocks data leakage to lower optimistic bias in biomedical ML
bioLeak: Leakage-Aware Modeling and Diagnostics for Machine Learning in R
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Bounds quantify error from restricted DAG search in MCMC
Restricted Search Space Graph MCMC via Birth-Death Processes
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Logistic kernel lets discrete sampler jump between isolated modes
Slithering Through Gaps: Capturing Discrete Isolated Modes via Logistic Bridging
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Different GGM methods yield varying AD brain connectivity estimates
Gaussian Graphical Models for Functional Connectivity Analysis: A Statistical Review with Applications to Alzheimer's Disease
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Hierarchical mass matrix unlocks closed-form leapfrog for RMHMC
Adaptive Riemannian Manifold Hamiltonian Monte Carlo with Hierarchical Metric
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Iterative closure resolves 80% of HTC-inconclusive causal edges
Iterative Identification Closure: Amplifying Causal Identifiability in Linear SEMs
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Sparse MCMC preconditioner learns correlations at O(m^2 d) cost
High-dimensional Adaptive MCMC with Reduced Computational Complexity
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Moment ratio identifies latent group effect from calibrated score
Identification of Latent Group Effects under Conditional Calibration
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Python tool fuses heart data types for earlier disease detection
mmid: Multi-Modal Integration and Downstream analyses for healthcare analytics in Python
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Unified framework equates NCE
A unifying view of contrastive learning, importance sampling, and bridge sampling for energy-based models
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Vine copulas build dependence trees from mixed EHR data
Vine Copulas for Analyzing Multivariate Conditional Dependencies in Electronic Health Records Data
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LASSO on Cholesky factor sparsifies multivariate spatial covariances
Regularized estimation for highly multivariate spatial Gaussian random fields
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Niching stabilizes importance sampling on multi-modal failure surfaces
Niching Importance Sampling for Multi-modal Rare-event Simulation
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S-learner ranks top 20% to capture 78% of campaign lift
A Large-Scale Empirical Comparison of Meta-Learners and Causal Forests for Heterogeneous Treatment Effect Estimation in Marketing Uplift Modeling
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Stochastic emulators reduce high-dimensional RBDO to deterministic optimization
High-dimensional reliability-based design optimization using stochastic emulators
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R package stops preprocessing from inflating ML scores
fastml: Guarded Resampling Workflows for Safer Automated Machine Learning in R
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One adapted mixture covers many state-space models
Unified Mixture Sampler for State-Space Models: Application to Stochastic Conditional Duration Models
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R package aggregates frequency tables with bounded disclosure risk
iLBA: An R package for confidentially disseminating aggregated frequency tables
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Mean-variance designs reduce utility variability
Mean--Variance Risk-Aware Bayesian Optimal Experimental Design for Nonlinear Models
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Multirate SVGD separates attraction and repulsion for stabler sampling
Multirate Stein Variational Gradient Descent for Efficient Bayesian Sampling
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LLM agents simulate immigration attitude shifts in social networks
LLM-Agent-based Social Simulation for Attitude Diffusion
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Infinitesimal jackknife matches bootstrap SEs from one MCMC run
Robust Standard Errors for Bayesian Posterior Functionals via the Infinitesimal Jackknife
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Pyomo.DoE adds E-optimality and ME-optimality via callbacks
Optimal Experimental Design using Eigenvalue-Based Criteria with Pyomo.DoE
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SMC samplers receive explicit finite-sample error bounds
On the complexity of standard and waste-free SMC samplers
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Inversion-free natural gradient converges on manifolds at O(log s/s^α)
Inversion-Free Natural Gradient Descent on Riemannian Manifolds
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Filter recovers optimal states from corrupted sensors as count rises
State estimations and noise identifications with intermittent corrupted observations via Bayesian variational inference
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Bayesian SEM now runs in seconds not hours
Implementation and Workflows for INLA-Based Approximate Bayesian Structural Equation Modelling
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Node-weighted priors on context trees yield exact Bayes factors
Context Tree Prior Distributions based on Node Weighting with exact Bayes Factors
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Bayesian model evolves node positions in weighted networks
A Bayesian Dynamic Latent Space Model for Weighted Networks
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R package recovers curves from summed functional data
FunctionalCalibration: an R package for estimation in aggregated functional data model
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Julia package samples unknown data streams in one pass with fixed memory
StreamSampling.jl: Efficient Sampling from Data Streams in Julia
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GPU speeds Bayesian spectral analysis over 500x
GPU-Accelerated Sequential Monte Carlo for Bayesian Spectral Analysis
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Bandit-guided distributed design cuts regret in black-box experiments
ALMAB-DC: Active Learning, Multi-Armed Bandits, and Distributed Computing for Sequential Experimental Design and Black-Box Optimization
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Adaptive sampling converges to ideal density for failure probs
Surrogate-Guided Adaptive Importance Sampling for Failure Probability Estimation
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Interpretable model matches large foundation models on retinal scans
Towards Interpretable Foundation Models for Retinal Fundus Images
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Full densities advance through random iterations without Monte Carlo
A Full-Density Approach to Simulating Random Iteration Equations with Applications
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Formulating Bayesian experimental design as a max-min game against an…
Maximin Robust Bayesian Experimental Design
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Bounds replace optimal transport for graph edge curvature
Edgewise Envelopes Between Balanced Forman and Ollivier-Ricci Curvature