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雷达卡
滑铁卢大学 本科,研究生
STAT 833 Stochastic Processes I F (0.5)
Random walks, renewal theory and processes and their application, Markov chains, branching processes, statistical inference for Markov chains.
随机过程:随机游走,更新理论和过程及其应用,马尔可夫链,分支过程,马尔可夫链统计推断.
北卡大学 高级研究生课程
232- STOCHASTIC PROCESSES
Prerequisites, Statistics 154 and 155. Advanced theoretic course including topics selected from: Foundations of stochastic processes, renewal processes, stationary processes, Markov processes, martingales, point processes. Kallianpur. (3).
随机过程:高级理论课程,包括:随机过程基础,更新过程,平稳过程,马尔可夫过程.
235- POINT PROCESSES
Prerequisite, Statistics 155. Random measures and point processes on general spaces, general Poisson and related processes, regularity, compounding. Point processes on the real line, stationarity and Palm distributions, Palm-Khintchine formulae. Convergence of point processes and related topics. Leadbetter. (3).
点过程: 一般空间的随机测度和点过程,一般泊松过程和相关过程,规则化,复合.实值点过程,平稳性和Palm 分布,Palm-Khintchine公式.点过程的收敛及相关话题.
236- STOCHASTIC ANALYSIS
Prerequisite, Statistics 154 amd 155, or permission of the instructor. Advanced course covering topics selected from: semimartingale theory, stochastic integrals, homogeneous chaos expansions, stochastic differential equations, Malliavin calculus, infinite dimensional processes, functional central limit theorems, Feynman-Kac formula, Feynman integral. Applications to filtering theory, infinite particle systems, quantum mechanics, and stochastic models in neurophysiology. Kallianpur. (3).
随机分析:高级课程,包括: 半鞅理论, 随机积分,齐次混沌展开,随机差分方程, Malliavin 微积分, ,有限维过程,泛函中心极限理论, Feynman-Kac公式, Feynman积分, 过滤理论的应用,无限粒子系统,量子力学,神经学中的随机模型.
密歇根大学
Statistics 526 (Math 526): DISCRETE STATE STOCHASTIC PROCESSES
Prerequisite: Statistics or Mathematics 525, or EECS 501, or permission. I, II. (3)
Generating functions; recurrent events and the renewal theorem; random walks, Markov chains; branching processes; limit theorems; Markov chains in continuous time with emphasis on birth and death processes and queueing theory. An introduction to Brownian motion, stationary processes, and martingales.
离散状态随机过程:生成函数, 常返事件和更新理论,随机游走,马氏链,分支过程,极限理论,强调生灭过程和排队论的连续时间的马氏链,布朗运动介绍,平稳过程,鞅.
芝加哥大学
312. Introduction to Stochastic Processes I. PQ: Stat 251, and Math 201 or 204. This course is an introduction to stochastic processes not requiring measure theory. Topics include branching processes, recurrent events, renewal theory, random walks, Markov chains, Poisson, and birth-and-death processes. Staff. Winter.
随机过程介绍:无需测度论知识的随机过程介绍.包括:分枝过程,常返事件,更新理论,随机游走,马尔可夫链,泊松过程和生灭过程.
313. Introduction to Stochastic Processes II. PQ: Stat 312 or consent of instructor. This course is a sequel to Stat 312. Topics covered include continuous time Markov chains: birth and death processes and queues, introduction to discrete time martingales, Brownian motion and diffusions. Time permitting: stochastic ordering, Poisson approximations. The emphasis is on defining the processes and calculating or approximating various related probabilities. The measure theoretic aspects of these processes is not covered rigorously. Staff. Spring.
随机过程简介II:包括:连续时间的马尔可夫链:生灭过程和排队过程,离散时间上的鞅,布朗运动和散射.时间允许的话,还包括随机排序,泊松近似.强调过程的定义以及相关概率的计算和近似.不包含测度论内容.
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时间序列分析(Time Series Analysis)
本科
威斯康星大学
349 Introduction to Time Series. II; 3 cr (N-A). Autocorrelation, elements of spectral analysis; dynamic models; auto-regressive and moving average models; identification and fitting; forecasting; seasonal adjustment; applications in the social sciences and environmental studies. P: Stat 301 or equiv, or cons inst.
时间序列引论:自相关,谱分析初步;动态模型;自回归和移动平均模型;识别和拟合;预测;季节调整;社会科学和环境研究中的应用.
UC-Berkley
Introduction to Time Series -- Statistics (STAT) 153 [4 units]
Description: An introduction to time series analysis in the time domain and spectral domain. Topics will include: estimation of trends and seasonal effects, autoregressive moving average models, forecasting, indicators, harmonic analysis, spectra.
时间序列引论:介绍时域和谱域的时间序列分析.包括:趋势和季节效应估计,自回归移动平均模型,预报,调和分析(harmonic analysis),谱.
哈佛大学
[Statistics 231. Bayesian Time Series]
A study of the dynamic linear model. Topics may include review of classical time series models, forecasting, smoothing, regression methods, polynomial trend models, seasonal models, and forecast monitoring and intervention. Theory and computational methods will be developed with an emphasis on applications to a variety of data sets.
Note: Expected to be given in 2000-01.
贝叶斯时间序列分析:动态线性模型研究.包括传统的时间序列模型,预测,平滑,回归方法,多项式趋势模型,季节模型,预测监控与干预.通过应用于数据集,发展计算理论与方法.
滑铁卢大学
STAT 443 F,W 3C 0.5
Forecasting
Model building. Multiple regression and forecasting. Exponential smoothing. Box-Jenkins models. Smoothing of seasonal data.
Prereq: STAT 331 or STAT 361 or consent of instructor
预测:建模.多元回归和预测.指数平滑.Box-Jenkins模型.季节数据的平滑.)
研究生
UC-Berkley
Analysis of Time Series -- Statistics (STAT) 248 [4 units]
Description: Frequency-based techniques of time series analysis, spectral theory, linear filters, estimation of spectra, estimation of transfer functions, design, system identification, vector-valued stationary processes, model building.
时间序列分析 :频率域的时间序列分析技术,谱理论,线性过滤法,谱估计,传递函数估计,设计,系统识别,向量值平稳过程(vector-valued stationary processes),建模.
宾夕法尼亚大学沃顿商学院
910. Forecasting and Time Series Analysis. (J) Staff. Prerequisite(s): STAT 511 or 541 or equivalent.
Fourier analysis of data, stationary time series, properties of autoregressive moving average models and estimation of their parameters, spectral analysis, forecasting. Discussion of applications to problems in economics, engineering, physical science, and life science.
预测与时间序列:数据的傅里叶分析,静态时间序列,ARMA模型的性质及其参数估计,谱分析,预测,;在经济,工程,物理和生命科学中的应用.)
密歇根大学
Statistics 531: STATISTICAL ANALYSIS OF TIME SERIES
Prerequisite: Statistics 511, or permission. I. (3)
Decomposition of series; trend and regression as a special case of time series; cyclic components; smoothing techniques; the variate difference method; representations including spectogram, periodogram, etc., stochastic difference equations, autoregressive schemes, moving averages; large sample inference and predictions; covariance structure and spectral densities; hypothesis testing and estimation; applications and other topics.
时间序列统计分析:序列的分解,作为时间序列特例的趋势与回归,循环组成,平滑技术,变差法; 谱图,周期图等方法展示,随机微分方程, 自回归方法,移动平均,大样本推断和预测,协方差结构和谱密度,假设检验和估计.
北卡大学
185- TIME SERIES AND MULTIVARIATE ANALYSIS
Prerequisite, Statistics 126. Time Series: Exploratory and graphical analysis; Time domain analysis and ARMA models; Fourier analysis: FFT, periodogram, smoothing; State space analysis: Kalman filter, dynamic models. Multivariate: Principal components, canonical correlation; Classification, clustering; Dimension reduction: projection pursuit, alternating conditional sliced inverse regression. Spring. Leadbetter, Simons. (3)
时间序列和多元分析: 时间序列:探索和图形分析;时域分析和ARMA 模型; 傅里叶分析: FFT, 周期图 平滑;状态空间分析: Kalman过滤法,动态模型.多元分析:主成分分析,典型相关分析;分类,聚类;降维:投影寻踪, 交替条件切片逆回归.
233- TIME SERIES ANALYSIS
Prerequisites, Statistics 185. Analysis of time series data by means of particular models such as autoregressive and moving average schemes. Spectral theory for stationary processes and associated methods for inference. Stationarity testing. Leadbetter. (3).
时间序列分析:运用自回归和移动平均模型进行时间序列分析.平稳过程的谱理论和相关的推断方法.平稳性检验.
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