This is an introductory probability course for students in engineering. It focuses mostly on random variables and their applications. Applications will be given in such areas as reliability theory, risk theory, inventory theory, failure models, stress models, computer science, and others.

This is an introductory probability course for students in engineering. It focuses mostly on random variables and their applications. Applications will be given in such areas as reliability theory, risk theory, inventory theory, failure models, stress models, computer science, and others.

An engineering-oriented introduction to safety and health problems encountered in industry and commerce. Coverage includes OSHA legislation, safety organization, programs and equipment, analytical approaches using probability and fault-tree models, human reliability; and cost-benefit analysis. Field study undertaken.

This course covers the fundamentals of probability and random processes useful in fields such as networks, communication, signal processing, and control. Sample space, events, probability law. Conditional probability. Independence. Random variables. Distribution, density functions. Random vectors. Law of large numbers. Central limit theorem. Estimation and detection. Markov chains.

Probability, joint, conditional. Statistical independence. Random variables, discrete and continuous. Distribution, density functions. Operations on a random variable, expectation, moments, characteristic functions. Law of Large Numbers. Central Limit Theorem. Random processes, wide-sense stationarity. Spectral characteristics of random processes. Gaussian, Poisson, and Markov processes. Queueing, Birth-Death processes.

This course covers the fundamentals of probability and random processes useful in fields such as networks, communication, signal processing, and control. Sample space, events, probability law. Conditional probability. Independence. Random variables. Distribution, density functions. Random vectors. Law of large numbers. Central limit theorem. Estimation and detection. Markov chains.

This course covers the fundamentals of probability and random processes useful in fields such as networks, communication, signal processing, and control. Sample space, events, probability law. Conditional probability. Independence. Random variables. Distribution, density functions. Random vectors. Law of large numbers. Central limit theorem. Estimation and detection. Markov chains.