Bayesian Inference in Processing Experimental Data

This report introduces general ideas and some basic methods of the Bayesian probability theory applied to physics measurements. Our aim is to make the reader familiar, through examples rather than rigorous formalism, with concepts such as: model comparison (including the automatic Ockham's Razor lter provided by the Bayesian approach); parametric inference; quanti cation of the uncertainty about the value of physical quantities, also taking into account systematic eects; role of marginalization; posterior characterization; predictive distributions; hierarchical modelling and hyperparameters; Gaussian approximation of the posterior and recovery of conventional methods, especially maximum likelihood and chisquare ts under well de ned conditions; conjugate priors, transformation invariance and maximum entropy motivated priors; Monte Carlo estimates of expectation, including a short introduction to Markov Chain Monte Carlo methods

Creative education at tokyo institute of technology

Technology are discussed. The basic educational principle is to give students as much hands-on experience as possible, and challenge them with assignments to design original mechanical systems. The curriculum includes the following: a course focused on the hands-on exercise of taking many types of machines apart, a course for groups of students to design and manufacture a `street performer robot ' consisting of mechanical and electrical components using computer control, and a course to design and draft original machines that satisfy pre-assigned objectives