Archive

「关于统计的资料整理汇总」
2024

LongitudinalDA 论文列表

List of Papers

2023

MS-Lec2.2 统计学基础（统计决策理论、统计推断概述、渐近准则与推断）

Fundamentals of Statistics (Statistical Decision Theory, Statistical Inference, Asymptotic Criteria and Inference)

MS-Lec2.1 统计学基础（总体、样本、模型、统计量、充分性和完备性）

Fundamentals of Statistics (Populations, Samples, Models, Statistics, Sufficiency, and Completeness)

MS-Lec1.3 概率论（渐近理论）

Probability (Asymptotic Theory)

MS-Lec1.2 概率论（分布及其特征、条件期望）

Probability (Distributions and Their Characteristics, Conditional Expectations)

MS-Lec1.1 概率论（概率空间、随机元、积分和微分）

Probability (Probability Spaces, Random Elements, Integration and Differentiation)

2022

RL-Lec12 基于策略的深度强化学习

Policy-Based Deep Reinforcement Learning

Bayes-Lec11 动态线性模型

Dynamic Linear Model

Bayes-Lec10 贝叶斯线性与广义线性模型

Bayesian Linear and Generalized Linear Model

RL-Lec11 基于Q函数的深度强化学习

Deep Reinforcement Learning with Q-Functions

Bayes-Lec9 贝叶斯层次模型

Bayesian Hierarchical Model

StatCompWithR: 统计计算函数细节

Statistical Computing with R (details about functions)

Bayes-Lec8_2 贝叶斯模型选择（下）

Bayesian Model Selection (II)

Bayes-Lec8_1 贝叶斯模型选择（上）

Bayesian Model Selection (I)

RL-Lec10 神经网络与深度学习

Neural Network and Deep Learning

Bayes-Lec7 贝叶斯近似推断

Bayesian Approximate Inference

RL-Lec9 探索与利用

Exploration and Exploitation

LRM-Lec4_1 参数估计（最小二乘估计与极大似然估计）

Parameter Estimation (LSE & MLE)

LRM-Lec3_2 随机向量（下）

Random Vector (II)

LRM-Lec3_1 随机向量（上）

Random Vector (I)

LRM-Lec2 矩阵论基础知识

Basic Notions in Matrix Theory

LRM-Lec1 线性模型概论

Introduction to Linear Models

Bayes-Lec6.2 MCMC方法及其实施

Introducton and implementation of MCMC

RL-Lec8 整合学习与规划

Integrating Learning and Planning

StatCompWithR: 统计计算（已完结）

Statistical Computing with R (done)

Bayes-Lec6.1 贝叶斯计算方法

Computing Method in Bayes Inference

RL-Lec7 策略梯度

Policy Gradient

Bayes-Lec5.2 共轭分布族

Conjecture Distribution Family

Bayes-Lec5.1 先验分布的选取

Selection of Prior Distribution

RL-Lec6 值函数估计

Value Function Approximation

RL-Lec5 时序差分学习

Temporal-Difference Learning

Bayes-Lec4.3 假设检验，区间估计和Minimax准则

Hypothesis, Interval Estimation, and Minimax Criterion

Bayes-Lec4.2 一般损失函数下的贝叶斯解

Bayes Solution Under Different Loss Function

Bayes-Lec4.1 贝叶斯统计决策的基本概念

Basic of Bayes Decision Theory

RL-Lec4 蒙特卡洛方法

Monte Carlo Reinforcement Learning

Bayes-Lec3.2 贝叶斯统计推断（假设检验与预测推断）

Hypothesis Testing and Prediction

Bayes-Lec3.1 贝叶斯统计推断（点估计与区间估计）

Point and Interval Estimation

Bayes-Lec2.3 多元正态分布与线性回归模型中参数的后验分布

Posterior Distribution of MVN and LR

RL-Lec3 动态规划

Dynamic Programming

Bayes-Lec2.2 常见分布参数的后验分布计算（续）

Posterior Distribution of Parameters of Common Distribution

RL-Lec2.2 最优策略下贝尔曼方程及MDP的扩展

Bellman Equation for Optimal Policy and Extension of MDPs

Bayes-Lec2.1 贝叶斯统计推断基础

Basic Bayes Statistical Reference

Bayes-Lec1 经典统计推断回顾

Review of Classical Statistics

RL-Lec2.1 马尔可夫决策过程

Markov Decision Process

RL-Lec1 强化学习介绍

Introduction to Reinforcement Learning