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Lecture Signals, systems & inference – Lecture 12: Probabilistic models, random variables

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Lecture Signals, systems & inference – Lecture 12: Probabilistic models, random variables. The following will be discussed in this chapter: Sample space and events, random variable, joint pdf, marginal pdfs. | Lecture Signals, systems & inference – Lecture 12: Probabilistic models, random variables Probabilistic models, random variables 6.011, Spring 2018 Lec 12 1 Sample space and events Sample space ° A specific Collection of outcome c outcomes (event) 2 Random variable ° Real line X(c) c 3 Joint pdf © The MathWorks, Inc. All rights reserved. This content is excluded 4 from our Creative Commons license. For more information, see https://ocw.mit.edu/help/faq-fair-use/ MultivariateNormalGaussian-Matlab Marginal pdfs 5 This image is in the public domain. Source: Wikimedia Commons MIT OpenCourseWare https://ocw.mit.edu 6.011 Signals, Systems and Inference Spring 2018 For information about citing these materials or our Terms of Use, visit: https://ocw.mit.edu/terms. 6

TÀI LIỆU LIÊN QUAN

Lecture Signals, systems & inference – Lecture 1: Introduction

Lecture Signals, systems & inference – Lecture 2: Transforms

Lecture Signals, systems & inference – Lecture 3: Energy spectral density

Lecture Signals, systems & inference – Lecture 4: State-Space Models

Lecture Signals, systems & inference – Lecture 5: State-space models, equilibrium, linearization

Lecture Signals, systems & inference – Lecture 6: Modal solution of undriven CT LTI state-space models

Lecture Signals, systems & inference – Lecture 7: Full modal solution, asymptotic stability, reachability and observability

Lecture Signals, systems & inference – Lecture 8: Matrix exponential, ZIR+ZSR, transfer function, hidden modes, reaching target states

Lecture Signals, systems & inference – Lecture 9: Observers for state estimation

Lecture Signals, systems & inference – Lecture 10: Observers, state feedback
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