QBUS3820 Lecture Notes - Lecture 5: Prior Probability, Asymptotically Optimal Algorithm, Bias Of An Estimator

• School: University of Sydney
• Department: Business Analytics
• Course: QBUS3820
• Professor: Peter

Document Summary

Discipline of business analytics, the university of sydney business school. Lecture 5: estimation methods: empirical risk minimisation, maximum likelihood estimation (mle, bayesian statistics. Empirical risk minimisation i=1 be the training data and let f (x; ) denote the. Let {(yi, xi)}n prediction functions, which depend on the parameter . The empirical risk minimisation estimation approach solves the following optimisation problem, in which l is the loss function: b = argmin. Nds the value of that minimises the function on the right-hand side. Example: least squares estimation under the squared error loss. Minimising the empirical risk will typically lead to over tting. In regularised risk minimisation, we estimate the model by solving. L(yi, f (xi; ))# + c( ), b = argmin where c( ) measures the complexity of the prediction function and is the weight for the complexity penalty c( ). Probability question: x counts the number of successes in 20 independent random trials probability of success (i. e. x has.