杜克大学统计学硕士专业的详细介绍
核心课程：
秋季学期
现代回归与预测建模 Modern Regression and Predictive Modeling
统计学编程 Programming for Statistical Science
贝叶斯与现代统计数据分析 Bayesian and Modern Statistical Data Analysis
春季学期：
高等贝叶斯推理与随机建模 Advanced Bayesian Inference and Stochastic Modeling
概率性的机器学习Probabilistic Machine Learning
研讨会：成为一个专业的统计员ProSeminar: Becoming a Professional Statistician
统计计算与计算 Statistical Computing and Computation
统计咨询研究 Statistical Consulting Workshop
选修课程：
秋季学期
统计推断理论Theory of Statistical Inference
计算生物学中的统计方法Statistical Methods in Computational Biology
应用随机过程Applied Stochastic Processes
统计决策理论Statistical Decision Theory
因果推理Causal Inference
广义线性模型Generalized Linear Models
空间统计Spatial Statistics
春季学期：
统计调查Statistics of Surveys
高级机器学习Advanced Machine Learning
DataFest
统计学习和贝叶斯非参数Statistical Learning and Bayesian Nonparametrics
时间序列与动态模型Time Series and Dynamic Models
现代实验设计Modern Design of Experiments
贝叶斯理论、统计推断基础及贝叶斯与非贝叶斯接口(Bayesian theory, foundations of statistical inference, Bayesian/non-Bayesian interfaces)
统计建模评估、选择与组合(Statistical model assessment, selection and combination)
数据挖掘与机器学习(Data mining and machine learning)
决策理论与应用风险分析(Decision theory and risk analysis with applications)
非参数贝叶斯方法(Nonparametric Bayesian methods)
图形建模(Graphical models)
蒙特卡罗模拟算法与理论(Monte Carlo simulation: algorithms and theory)
分布式与集群统计计算(Distributed and cluster statistical computing)
高维统计建模与大型数据集(High-dimensional statistical models and large data sets)
时间序列分析与预测(Time series analysis and forecasting)
大规模统计混合建模(Large-scale statistical mixture modeling)
抽样、调查方法与数据缺失插补(Sampling, survey methods and missing data imputation)
空间统计建模与空间随机过程(Spatial statistical modeling and spatial stochastic processes)
随机过程与领域的模拟与推理(Simulation and inference for stochastic processes and random fields)
时间序列与随机领域极限值(Extreme values in time series and random fields)
几何与拓扑(统计学)及形态分析(Geometry and topology in statistics; shape analysis)
复杂等级与潜在变量建模(Complex hierarchical and latent variable modeling)
多尺度方法(Multiscale/multiresolution methods)
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