Introduction to the 53rd Karl Terzaghi Lecture

Karl Popper – Introduction Sir

Popper commanded international audiences and conversation with him was an intellectual adventure even if a little rough animated by a myriad of philosophical problems. His intense desire to tear away at the veneer of falsity in pursuit of the truth lead him to contribute to a field of thought encompassing (among others) political theory, quantum mechanics, logic, scientific method and evolution...

Lecture II : The Theories of Karl Marx

Introduction to a Festschrift for Karl Shell

This special volume of the Journal of Economic Theory honors Karl Shell, the journal’s founding editor, on the occasion of his 65th birthday. The contributions to the volume are from Karl’s students or co-authors. Karl’s generosity and intellect have touched our lives profoundly. His keen sense of what matters most in economic theory has shaped our own research, and has influenced the entire ec...

Karl Sigman 1 Introduction to reducing variance in Monte

Typically, we would use (say) α = 0.05 in which case zα/2 = z0.025 = 1.96, and we thus obtain a 95% confidence interval X(n)± (1.96) σ √ n . The length of the confidence interval is 2(1.96) σ √ n which of course tends to 0 as the sample size n gets larger. In practice we would not actually know the value of σ2; it would be unknown (just as μ is). But this is not really a problem: we instead use...

Introduction to Probability: Lecture Notes

A probabilistic model of an experiment is defined by a probability space consisting of a set (sample space Ω) of sample points or outcomes (exhaustive collection of elementary outcomes of the experiment) and a probability law P which assigns to each event in (subset of) Ω a probability satisfying three axioms: (1) nonnegativity, i.e., all probabilities are nonnegative; (2) additivity, i.e., the...