Probability

2022 Fall
#22991

Concepts of Probability

Adam R Lucas
Aug 24, 2022 - Dec 09, 2022
Mo, We, Fr
09:00 am - 09:59 am

Instruction Mode: In-Person Instruction

Open Seats

18 Unreserved Seats

STAT 134 - LEC 001 Concepts of Probability more detail
An introduction to probability, emphasizing concepts and applications. Conditional expectation, independence, laws of large numbers. Discrete and continuous random variables. Central limit theorem. Selected topics such as the Poisson process, Markov chains, characteristic functions.
2022 Fall
#22988

Concepts of Probability

Daniel Cyrus Raban
Aug 24, 2022 - Dec 09, 2022
Mo, We
03:00 pm - 03:59 pm

Instruction Mode: In-Person Instruction

Open Seats

2 Unreserved Seats

STAT 134 - DIS 106 Concepts of Probability more detail
An introduction to probability, emphasizing concepts and applications. Conditional expectation, independence, laws of large numbers. Discrete and continuous random variables. Central limit theorem. Selected topics such as the Poisson process, Markov chains, characteristic functions.
2022 Fall
#22987

Concepts of Probability

Daniel Cyrus Raban
Aug 24, 2022 - Dec 09, 2022
Mo, We
02:00 pm - 02:59 pm

Instruction Mode: In-Person Instruction

Open Seats

1 Unreserved Seats

STAT 134 - DIS 105 Concepts of Probability more detail
An introduction to probability, emphasizing concepts and applications. Conditional expectation, independence, laws of large numbers. Discrete and continuous random variables. Central limit theorem. Selected topics such as the Poisson process, Markov chains, characteristic functions.
2022 Fall
#22986

Concepts of Probability

Seunghoon Paik
Aug 24, 2022 - Dec 09, 2022
Mo, We
01:00 pm - 01:59 pm

Instruction Mode: In-Person Instruction

Open Seats

4 Unreserved Seats

STAT 134 - DIS 104 Concepts of Probability more detail
An introduction to probability, emphasizing concepts and applications. Conditional expectation, independence, laws of large numbers. Discrete and continuous random variables. Central limit theorem. Selected topics such as the Poisson process, Markov chains, characteristic functions.
2022 Fall
#27871

Probability and Risk Analysis for Engineers

Alexandra Novales
Aug 24, 2022 - Dec 09, 2022
Fr
10:00 am - 10:59 am
Social Sciences Building 126

Instruction Mode: In-Person Instruction

Open Seats

11 Unreserved Seats

INDENG 172 - DIS 102 Probability and Risk Analysis for Engineers more detail
This is an introductory course in probability designed to develop a good understanding of uncertain phenomena and the mathematical tools used to model and analyze it. Applications will be given in such areas as reliability theory, risk theory, inventory theory, financial models, and computer science, among others. To complement the theory, the course also covers the basics of stochastic simulation. This course is a probability course and cannot be used to fulfill any engineering unit or elective requirements.
2022 Fall
#27870

Probability and Risk Analysis for Engineers

Alexandra Novales
Aug 24, 2022 - Dec 09, 2022
Fr
09:00 am - 09:59 am

Instruction Mode: In-Person Instruction

Open Seats

11 Unreserved Seats

INDENG 172 - DIS 101 Probability and Risk Analysis for Engineers more detail
This is an introductory course in probability designed to develop a good understanding of uncertain phenomena and the mathematical tools used to model and analyze it. Applications will be given in such areas as reliability theory, risk theory, inventory theory, financial models, and computer science, among others. To complement the theory, the course also covers the basics of stochastic simulation. This course is a probability course and cannot be used to fulfill any engineering unit or elective requirements.
2022 Fall
#27869

Probability and Risk Analysis for Engineers

Rhonda Lee Righter
Aug 24, 2022 - Dec 09, 2022
Tu, Th
11:00 am - 12:29 pm

Instruction Mode: In-Person Instruction

Open Seats

INDENG 172 - LEC 001 Probability and Risk Analysis for Engineers more detail
This is an introductory course in probability designed to develop a good understanding of uncertain phenomena and the mathematical tools used to model and analyze it. Applications will be given in such areas as reliability theory, risk theory, inventory theory, financial models, and computer science, among others. To complement the theory, the course also covers the basics of stochastic simulation. This course is a probability course and cannot be used to fulfill any engineering unit or elective requirements.
2022 Fall
#28963

Probability and Random Processes

Aug 24, 2022 - Dec 09, 2022
We
12:00 pm - 12:59 pm

Instruction Mode: In-Person Instruction

No Open Seats
EECS 126 - DIS 202 Probability and Random Processes more detail
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.
2022 Fall
#28089

Probability and Random Processes

Aug 24, 2022 - Dec 09, 2022
Fr
12:00 pm - 12:59 pm
Social Sciences Building 170

Instruction Mode: In-Person Instruction

No Open Seats
EECS 126 - DIS 204 Probability and Random Processes more detail
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.
2022 Fall
#28088

Probability and Random Processes

Aug 24, 2022 - Dec 09, 2022
We
08:00 am - 08:59 am

Instruction Mode: In-Person Instruction

Open Seats

2 Unreserved Seats

EECS 126 - DIS 203 Probability and Random Processes more detail
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.