Probabilistic Systems Analysis and Applied Probability

Conceptual:

• Master the basic concepts associated with probability models.
• Be able to translate models described in words to mathematical ones.
• Understand the main concepts and assumptions underlying Bayesian and classical inference.
• Obtain some familiarity with the range of applications of inference methods.

More technical:

• Become familiar with basic and common probability distributions.
• Learn how to use conditioning to simplify the analysis of complicated models.
• Have facility manipulating probability mass functions, densities, and expectations.
• Understand the power of laws of large numbers and be able to use them when appropriate.
• Develop a solid understanding of the concept of conditional expectation and its role in inference.
• Learn how to formulate simple dynamical models as Markov chains and analyze them.
• Become familiar with the basic inference methodologies (for both estimation and hypothesis testing) and be able to apply them.