1.Main Content:Conditional probabilities,Bayes'formula.Independent events 2.Basic Concepts and Knowledge Points:The concept of conditional probabilities,the ideas and applications of the Bayes'formula,the concept of independence and the analysis of independent events 3.Problems and Application (Ability Requirements):Using the conditional probability formula and Bayes'formula to analyze some probability problems (3)Thinking and Practice Some further reading and studying on the Bayes networks.A discussion on the Monty Hall problem(three gate problem).Ideological and political education:why telling lies repeatedly lower one's credibility? (4) Teaching Methods and Approaches The main methods and approaches used in this Module is in-class teaching. multimedia teaching.network-assisted teaching.and class discussion Module 4 Random variables (1)Purpose and Requirements 1.To comprehend the concept of random variables and discrete random variables 2. To comprehend the concept of expectation and variance 3.To master Bemnoulli and binomial probability distributions 4.To master Poisson probability distribution 5.To master several other important probability distributions 6.To comprehend the concept of and to master the computation of cumulative distribution (2) Contents Section 1 1.Main Content:Random variables,Discrete Random variables,expectation value,expectation value of a function,variance,Bemoulli and binomial random variables.Poisson random variable.some other discrete probability distribution,expected value of sums of random variables cumulative distribution function 2.Basic Concepts and Knowledge Points:Random variable,discrete random variable,expectation value,variance,Bernoulli random variable,binomial 44 1．Main Content: Conditional probabilities, Bayes’ formula, Independent events 2．Basic Concepts and Knowledge Points: The concept of conditional probabilities, the ideas and applications of the Bayes’ formula, the concept of independence and the analysis of independent events 3．Problems and Application (Ability Requirements): Using the conditional probability formula and Bayes’ formula to analyze some probability problems (3) Thinking and Practice Some further reading and studying on the Bayes networks. A discussion on the Monty Hall problem (three gate problem). Ideological and political education: why telling lies repeatedly lower one’s credibility? (4) Teaching Methods and Approaches The main methods and approaches used in this Module is in-class teaching, multimedia teaching, network-assisted teaching, and class discussion. Module 4 Random variables (1) Purpose and Requirements 1．To comprehend the concept of random variables and discrete random variables 2．To comprehend the concept of expectation and variance 3．To master Bernoulli and binomial probability distributions 4．To master Poisson probability distribution 5．To master several other important probability distributions 6．To comprehend the concept of and to master the computation of cumulative distribution (2) Contents Section 1 1．Main Content: Random variables, Discrete Random variables, expectation value, expectation value of a function, variance, Bernoulli and binomial random variables, Poisson random variable, some other discrete probability distribution, expected value of sums of random variables, cumulative distribution function 2．Basic Concepts and Knowledge Points: Random variable, discrete random variable, expectation value, variance, Bernoulli random variable, binomial