A statistical analysis of probabilistic counting algorithms

An integrative approach based on probabilistic modelling and statistical inference for morpho-statistical characterization of astronomical data

This paper describes several applications in astronomy and cosmology that are addressed using probabilistic modelling and statistical inference.

Distribution Estimation for Probabilistic Loops

We present an algorithmic approach to estimate the value distributions of random variables of probabilistic loops whose statistical moments are (partially) known. Based on these moments, we apply two statistical methods, Maximum Entropy and Gram-Charlier series, to estimate the distributions of the loop's random variables. We measure the accuracy of our distribution estimation by comparing the resulting distributions using exact and estimated moments of the probabilistic loop, and performing statistical tests. We evaluate our method on several probabilistic loops with polynomial updates over r…

形状异质性下计数过程的统计推断

Statistical inference for counting processes under shape heterogeneity

针对比例速率模型的形状假设局限性，提出用形状和规模双指标刻画计数过程速率函数与协变量的关系，设计对应估计方法并验证其有效性。

Direct and approximately valid probabilistic inference on a class of statistical functionals

Existing frameworks for probabilistic inference assume the quantity of interest is the parameter of a posited statistical model. In machine learning applications, however, often there is no statistical model/parameter; the quantity of interest is a statistical functional, a feature of the underlying distribution. Model-based methods can only handle such problems indirectly, via marginalization from a model parameter to the real quantity of interest. Here we develop a generalized inferential model (IM) framework for direct probabilistic uncertainty quantification on the quantity of interest. In…

面向概率型电价预测的统计模型与深度学习模型中的不确定性量化分析

Analyzing Uncertainty Quantification in Statistical and Deep Learning Models for Probabilistic Electricity Price Forecasting

本研究针对德国市场电价预测，对比分析了结合不同不确定性量化方法的LEAR统计模型与DDNN深度学习模型，发现两类模型各有优势，共形预测的不确定性捕捉效果最佳。

一种用于概率分析与预测的统计降复杂度气候模型

A Statistical Reduced Complexity Climate Model for Probabilistic Analyses and Projections

本文提出一种统计降复杂度气候模型，将全球气候物理方程表示为非线性状态空间形式，并用扩展卡尔曼滤波和最大似然法估计参数，基于1959-2022年数据预测至2100年全球平均地表温度。