Sequential Selection for Accelerated Life Testing via Approximate Bayesian Inference

Bayesian mode and maximum estimation and accelerated rates of contraction

We study the problem of estimating the mode and maximum of an unknown regression function in the presence of noise. We adopt the Bayesian approach by using tensor-product B-splines and endowing the coefficients with Gaussian priors. In the usual fixed-in-advanced sampling plan, we establish posterior contraction rates for mode and maximum and show that they coincide with the minimax rates for this problem. To quantify estimation uncertainty, we construct credible sets for these two quantities that have high coverage probabilities with optimal sizes. If one is allowed to collect data sequential…

Accelerated structured matrix factorization

Matrix factorization exploits the idea that, in complex high-dimensional data, the actual signal typically lies in lower-dimensional structures. These lower dimensional objects provide useful insight, with interpretability favored by sparse structures. Sparsity, in addition, is beneficial in terms of regularization and, thus, to avoid over-fitting. By exploiting Bayesian shrinkage priors, we devise a computationally convenient approach for high-dimensional matrix factorization. The dependence between row and column entities is modeled by inducing flexible sparse patterns within factors. The av…

Lifelong Generative Modeling

Lifelong learning is the problem of learning multiple consecutive tasks in a sequential manner, where knowledge gained from previous tasks is retained and used to aid future learning over the lifetime of the learner. It is essential towards the development of intelligent machines that can adapt to their surroundings. In this work we focus on a lifelong learning approach to unsupervised generative modeling, where we continuously incorporate newly observed distributions into a learned model. We do so through a student-teacher Variational Autoencoder architecture which allows us to learn and pres…

基于Weibull分布的似然回归在受控数据下的加速寿命测试模型

Likelihood-Based Regression for Weibull Accelerated Life Testing Model Under Censored Data

本文提出了一种基于Weibull分布的似然回归方法，用于在删失数据下建模应力依赖的可靠性行为。

对数正态寿命区间监测下循环应力加速寿命试验的稳健推断

Robust inference for cyclic-stress accelerated life tests under interval monitoring with lognormal lifetimes

针对区间删失的对数正态寿命循环应力加速寿命试验，提出基于加权密度功率散度的稳健估计方法，推导渐近分布并给出置信区间，模拟和实例验证了抗异常值能力。

基于区间监测和Weibull寿命分布的步进应力加速寿命试验的稳健Rao型检验

Robust Rao-type tests for step-stress accelerated life-tests under interval-monitoring and Weibull lifetime distributions

本文针对区间删失数据下的Weibull步进应力加速寿命试验，基于密度功率散度提出稳健限制性估计量，并构建稳健Rao型检验以检验模型参数的复合原假设。

Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space

Chemical space is so large that brute force searches for new interesting molecules are infeasible. High-throughput virtual screening via computer cluster simulations can speed up the discovery process by collecting very large amounts of data in parallel, e.g., up to hundreds or thousands of parallel measurements. Bayesian optimization (BO) can produce additional acceleration by sequentially identifying the most useful simulations or experiments to be performed next. However, current BO methods cannot scale to the large numbers of parallel measurements and the massive libraries of molecules cur…

基于有限混合失败率的异质步应力加速寿命测试（h-SSALT）模型

A Finite Mixture Failure-rate based Heterogeneous Step-stress Accelerated Life Testing (h-SSALT) Model

本文提出一种基于有限混合失败率的异质步应力加速寿命测试模型，通过Weibull分布的II型截尾失效时间，允许在第二应力水平上通过有限混合的m个潜在子组产生异质性，利用期望最大化算法进行最大似然估计，并通过模拟研究验证了忽略异质性对寿命预测的系统性偏差。

A Broad and General Sequential Sampling Scheme

In this paper, we propose a broad and general sequential sampling scheme, which incorporates four different types of sampling procedures: i) the classic Anscombe-Chow-Robbins purely sequential sampling procedure; ii) the ordinary accelerated sequential sampling procedure; iii) the relatively new k-at-a-time purely sequential sampling procedure; iv) the new k-at-a-time improved accelerated sequential sampling procedure. The first-order and second-order properties of this general sequential sampling scheme are fully investigated with two illustrations on minimum risk point estimation for the mea…

函数加速失效时间模型的高效估计

Efficient Estimation for Functional Accelerated Failure Time Model

本文提出函数加速失效时间模型，结合筛极大似然方法高效估计参数，克服捆绑参数挑战，证明估计量渐近性质，并通过模拟和实际数据验证。

Improving inference for nonlinear state-space models of animal population dynamics given biased sequential life stage data

State-space models (SSMs) are a popular tool for modeling animal abundances. Inference difficulties for simple linear SSMs are well known, particularly in relation to simultaneous estimation of process and observation variances. Several remedies to overcome estimation problems have been studied for relatively simple SSMs, but whether these challenges and proposed remedies apply for nonlinear stage-structured SSMs, an important class of ecological models, is less well understood. Here we identify improvements for inference about nonlinear stage-structured SSMs fit with biased sequential life st…

带过度分散部分观测的随机仓室模型的加速推断

Accelerated inference for stochastic compartmental models with over-dispersed partial observations

针对带观测过度分散的部分观测随机仓室模型，提出基于LawPAL和拉普拉斯近似的快速确定性推断方法，兼具渐近精确性与计算效率，并嵌入Stan构建瑞士新冠疫情实用模型。