Lecture 6.1: a. Review: the abstract probability space — [Probability | Santosh S. Venkatesh] скачать в хорошем качестве

Lecture 6.1: a. Review: the abstract probability space — [Probability | Santosh S. Venkatesh]

This is the first lecture in the sixth tableau in a sequence of 12 tableaux comprising a suite of lectures on probability. The viewer will find these lectures organised by tableau in distinct playlists whose titles are numbered sequentially from 1 through 12: "Probability Tableau 1 – 12: ⋯ title ⋯ — [Probability | Santosh S. Venkatesh]". For the viewer who prefers all her videos in one place they are also available in a single playlist: "Probability | Santosh S. Venkatesh, University of Pennsylvania [FULL COURSE]”. The viewer new to this sequence should begin by casting an eye over the introductory videos in Tableau 1 which outline the organisation of videos in the entire suite, the philosophy behind these lectures, what a viewer may hope to learn from them, and the notational conventions in force. The suite is intended to be watched sequentially from Tableau 1 through Tableau 12 and for someone new to the subject that is likely to be the best strategy. There are delicious bon mots scattered here and there for the expert as well, however, and any such may, of course, wish to flit here and there through the suite as interest and time allow. Tableau 6, of which this is the first lecture, consists of two sub-Tableaux. The 17 lectures in this tableau introduce the viewer to formal mechanisms which codify raw intuition in the simplest chance settings. Sub-Tableau 6.1 has six lectures labelled 6.1: a – f. These video lectures summarise the lessons that may be accrued from the simplest chance problems and introduce the viewer to random choice in formal discrete spaces, the notions of atoms and mass functions, and the common distributions — combinatorial, binomial, Poisson, and geometric. Sub-Tableau 6.2 is an optional "dangerous bend" collection of eleven lectures 6.2: a – k which may be skipped by the viewer in a hurry to get to the res gestae. For the viewer who has had exposure to integral calculus this collection of lectures segue from chance experiments in discrete domains to chance experiments in the continuum in one and more dimensions. The user will see mass functions segue to probability densities and encounter the basic densities — uniform, exponential, and normal. ************************************************************************************************* In this, Lecture 6.1: a, the first lecture in the sequence of lectures comprising Tableau 6, to set the stage, the viewer is invited to review the key features of the abstract probability space and is led to ask how probability measure may be consistently constructed.