Conceptual
Browse Instruct Curate
Login

Average-Case and Probabilistic Analysis of Algorithms

Beyond worst-case bounds, average-case analysis measures the expected cost of an algorithm over a probability distribution on inputs. This area covers modelling random inputs, computing expectations of cost measures (such as the number of runs in a random binary string), linearity of expectation and indicator variables, and how average-case results can differ from or confirm worst-case behaviour. Students learn to set up and evaluate the expected running time of an algorithm over a random-input model.