Bayesian Estimation for a Novel Geometric Integer-Valued Generalized Autoregressive Conditional Heteroscedastic (NoGe-INGARCH) Model
The authors propose a novel geometric integer-valued generalized autoregressive conditional heteroscedastic (NoGe-INGARCH) model and develop Bayesian estimation techniques using the Hamiltonian Monte Carlo (HMC) algorithm to estimate the model parameters.
Efficient Spatial Simulation of Extreme High-Resolution Radar Precipitation Data Using Integrated Nested Laplace Approximations (INLA)
A computationally efficient framework for modelling and simulating extreme spatial precipitation using high-resolution radar data, capturing both marginal and tail dependence properties.
条件付きサンプリングを可能にする生成型拡散モデル
生成型拡散モデルは、複雑な高次元分布をモンテカルロサンプリングで近似する強力なクラスのサンプラーである。しかし、条件付き分布のサンプリングは重要な課題として残されている。本論文では、条件付き分布のサンプリングを可能にする計算手法を包括的に紹介する。
Generative Diffusion Models for Conditional Sampling: A Comprehensive Review
Generative diffusion models are a powerful class of Monte Carlo samplers that can be extended to sample from conditional distributions, which is crucial for applications such as Bayesian inverse problems.
Anytime-Valid Sequential t-Tests and Confidence Sequences for Gaussian Means with Unknown Variance
This paper develops new e-processes and confidence sequences for sequential t-tests on the mean of a Gaussian distribution with unknown variance. The methods provide anytime-valid, nonasymptotic error control and optimal asymptotic performance.
Efficient Parallel Computation of Similarity Matrices from Piecewise Constant Functions
We present a computational framework for efficiently processing piecewise constant functions (PCFs) in parallel on CPUs and GPUs. The framework enables fast computation of similarity matrices, averages, standard deviations, and other statistical measures on large datasets of PCFs.
Score Operator Newton Transport: Sampling and Bayesian Computation Approach
提案された新しいサンプリングおよびベイズ計算アプローチは、スコアオペレーターニュートン輸送を使用して、効率的なベイジアン推論と他のサンプリングタスクに新しいアルゴリズムを提供します。
A Bayesian Framework for Measuring Association and Emotional Dynamics in Web Discourse
The author presents a Bayesian framework to measure association between categorical variables, focusing on emotional dynamics in web discourse. The approach provides detailed insights into the relationships between emotions.
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