几年来，每年都会收到这门课的委托，通常最开始有几次小考试每次一小时多点，难度逐步提升，需要专业学统计的专家来handle此类问题

扫一扫又不会怀孕，扫一扫，作业无烦恼。
留学顾问の QQ：2128789860
留学顾问の微信：risepaper

课本是用的统计学完全教程 All of Statistics A Concise Course in Statistical Inference

Probability.- Random Variables.- Expectation.- Inequalities.- Convergence of Random Variables.- Models, Statistical Inference and Learning.- Estimating the CDF and Statistical Functionals.- The Bootstrap.- Parametric Inference.- Hypothesis Testing and p-values.- 代写Bayesian Inference.- Statistical Decision Theory.- Linear and Logistic Regression.- Multivariate Models.- Inference about Independence.- Causal Inference.- Directed Graphs and Conditional Independence.- Undirected Graphs.- Loglinear Models.- Nonparametric Curve Estimation.- Smoothing Using Orthogonal Functions.- Classification.- Probability Redux: Stochastic Processes.- Simulation Methods.

Probability is a mathematical language for quantifying uncertainty. In this Chapter we introduce the basic concepts underlying probability theory. We begin with the sample space, which is the set of possible outcomes.

Bayes’ theorem is the basis of “expert systems” and “Bayes’ nets,” which are discussed in Chapter 17. First, we need a preliminary result.

ANDERSON, T. W. (1984). An Introduction to Multivariate Statistical Analysis (Second Edition). Wiley.
BARRON, A., SCHERVISH, M. J. and WASSERMAN, L. (1999). The consistency of posterior distributions in nonparametric problems. The Annals of Statistics 27 536-561.