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A free and open online publication of educational material from thousands of MIT courses, covering the entire MIT curriculum, ranging from introductory to the most advanced graduate courses. On the OCW website, each course includes a syllabus, instructional material like notes and reading lists, and learning activities like assignments and solutions. Some courses also have videos, online textbooks, and faculty insights on teaching. Knowledge is your reward. There's no signup or enrollment, and no start or end dates. OCW is self-paced learning at its best. Whether you’re a student, a teacher, or simply a curious person that wants to learn, MIT OpenCourseWare (OCW) offers a wealth of insight, inspiration, videos, and a whole lot more! Get the full picture on the OCW website at https://ocw.mit.edu. Accessibility: https://accessibility.mit.edu/ User comments policy: https://ocw.mit.edu/comments/ (Channel banner photo by Nietnagel on Flickr: https://flic.kr/p/8WXxfK.)
Episodes


S07.3 Independence of Random Variables Versus Independence of Events

L05.9 Elementary Properties of Expectation

L15.4 The Case of Multiple Observations

L07.4 Independence of Random Variables

L08.9 Calculation of Normal Probabilities

L21.8 Merging of Bernoulli Processes

L09.10 Joint CDFs

L25.5 Recurrent and Transient States: Review

L07.2 Conditional PMFs

L03.6 Independence Versus Conditional Independence

L15.2 Recognizing Normal PDFs

L22.3 Applications of the Poisson Process

L01.8 A Continuous Example

L11.2 The PMF of a Function of a Discrete Random Variable

L09.5 Total Probability & Expectation Theorems

L06.6 Geometric PMF Memorylessness & Expectation

L02.4 Conditional Probabilities Obey the Same Axioms

L07.8 The Hat Problem

L04.3 Die Roll Example
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