In measurements, the measurement obtained can suffer from two types of uncertainties. The first is the random uncertainty which is due to the noise in the process and the measurement. The second contribution is due to the systematic uncertainty which may be present in the measuring instrument. Systematic errors, if detected, can be easily compensated as they are usually constant throughout the measurement process as long as the measuring instrument and the measurement process are not changed. But it can not be accurately known while using the instrument if there is a systematic error and if there is, how much? Hence, systematic uncertainty could be considered as a contribution of a fuzzy nature. This systematic error can be approximately modeled based on our past data about the measuring instrument and the process. Statistical methods can be used to calculate the total uncertainty from both systematic and random contributions in a measurement. But, the computational complexity is very high and hence, are not desirable. L.A.Zadeh introduced the concepts of fuzzy variables and fuzzy sets. Fuzzy variables are based on the theory of possibility and hence are possibility distributions. This makes them suitable to handle any type of uncertainty, i.e., both systematic and random contributions to the total uncertainty. Random-fuzzy variable (RFV) is a type 2 fuzzy variable, defined using the mathematical possibility theory, used to represent the entire information associated to a measurement result. It has an internal possibility distribution and an external possibility distribution called membership functions. The internal distribution is the uncertainty contributions due to the systematic uncertainty and the bounds of the RFV are because of the random contributions. The external distribution gives the uncertainty bounds from all contributions. (Wikipedia).
Statistics: Ch 5 Discrete Random Variable (1 of 27) What is a Random Variable?
Visit http://ilectureonline.com for more math and science lectures! To donate: http://www.ilectureonline.com/donate https://www.patreon.com/user?u=3236071 We will learn a random variable is a variable which represents the outcome of a trial, an experiment, or an event. It is a specific n
From playlist STATISTICS CH 5 DISCRETE RANDOM VARIABLE
Random Variable Examples with Discrete and Continuous
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Random Variable Examples with Discrete and Continuous
From playlist Statistics
Statistics: Ch 5 Discrete Random Variable (2 of 27) What is a Discrete Random Variable?
Visit http://ilectureonline.com for more math and science lectures! To donate: http://www.ilectureonline.com/donate https://www.patreon.com/user?u=3236071 We will learn a discrete random variable can be a count of something, an integer, as how many times a coin comes up “heads” or “tails
From playlist STATISTICS CH 5 DISCRETE RANDOM VARIABLE
Prob & Stats - Random Variable & Prob Distribution (1 of 53) Random Variable
Visit http://ilectureonline.com for more math and science lectures! In this video I will define and gives an example of what is a random variable. Next video in series: http://youtu.be/aEB07VIIfKs
From playlist iLecturesOnline: Probability & Stats 2: Random Variable & Probability Distribution
Conceptual Questions about Random Variables and Probability Distributions
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Conceptual Questions about Random Variables and Probability Distributions
From playlist Statistics
(PP 3.1) Random Variables - Definition and CDF
(0:00) Intuitive examples. (1:25) Definition of a random variable. (6:10) CDF of a random variable. (8:28) Distribution of a random variable. A playlist of the Probability Primer series is available here: http://www.youtube.com/view_play_list?p=17567A1A3F5DB5E4
From playlist Probability Theory
What are Continuous Random Variables? (1 of 3: Relation to discrete data)
More resources available at www.misterwootube.com
From playlist Random Variables
Prob & Stats - Random Variable & Prob Distribution (2 of 53) Random Variable - Terminology Review
Visit http://ilectureonline.com for more math and science lectures! In this video I will define and reviews terminologies associated with random variables. Next video in series: http://youtu.be/ebP7x2zviBI
From playlist iLecturesOnline: Probability & Stats 2: Random Variable & Probability Distribution
More Joint Continuous [Normal] Random Variables
I recently uploaded 200 videos that are much more concise with excellent graphics. Click the link in the upper right-hand corner of this video. It will take you to my youtube channel where videos are arranged in playlists. In this older video: explanation & word problem involving the ne
From playlist Unit 6 Probability B: Random Variables & Binomial Probability & Counting Techniques
Fuzzy Logic Systems - Part 6: Three Fuzzy Inference Systems
This video is about Fuzzy Logic Systems - Part 6: Three Fuzzy Inference Systems
From playlist Fuzzy Logic
DEFCON 14: The Evolving Art of Fuzzing
Speaker: Jared DeMott, Vulnerability Researcher, Applied Security, Inc. Abstract: The Evolving Art of Fuzzing will be a technical talk detailing the current state of fuzzing and describing cutting edge techniques. Fuzzer types, metrics, and future research will be presented. Also, three o
From playlist DEFCON 14
Approaching (almost) Any Machine Learning Problem | by Abhishek Thakur | Kaggle Days Dubai | Kaggle
Abhishek Thakur is the chief data scientist at https://boost.ai building state-of-the-art chatbots primarily for banking and insurance industries. His passion lies in solving difficult world problems through data science. He is the co-organizer of the Berlin Machine Learning Meetup and not
From playlist Kaggle Days Dubai Edition | by LogicAI + Kaggle
Rasa Livecoding: Adding an Action Server To Rasa X (Docker Compose)
Please note that the Community Edition (free version) of ‘Rasa X’ is no longer supported by Rasa. You can learn more here: https://rasa.com/blog/rasa-x-community-edition-changes/ Now that our server is live, we'll be adding our action server to it so that our assistant can query a databas
From playlist Live Coding
Denjoe O’Connor - Non-perturbative Studies of Membrane Matrix Models
https://indico.math.cnrs.fr/event/4272/attachments/2260/2719/IHESConference_Denjoe_OCONNOR.pdf
From playlist Space Time Matrices
Jeremy Quastel (Toronto) -- Convergence of finite range exclusions to the KPZ fixed point
We will describe a method of comparison with TASEP which proves that both the KPZ equation and finite range exclusion models converge to the KPZ fixed point. For the KPZ equation and the nearest neighbour exclusion, the initial data is allowed to be a continuous function plus a finite num
From playlist Columbia Probability Seminar
Machine Learning Crash Course-2 Hours | Learn Machine Learning | Machine Learning Tutorial | Edureka
🔥 Post Graduate Diploma in Artificial Intelligence by E&ICT Academy NIT Warangal: https://www.edureka.co/executive-programs/machine-learning-and-ai 🔥 Topics Wise Machine Learning Podcast : https://castbox.fm/channel/id1832236?country=us This Edureka Machine Learning video on "Machine Lear
From playlist Machine Learning Algorithms in Python (With Demo) | Edureka
Polls vs Win Charts - Positional Heuristics - Extra Politics - #2
Extra Politics is an 8-episode mini-series exploring the United States political system from a game design perspective. Today we discuss what a win chart is, and how win charts explain why election polls aren't always accurate--or in some cases, completely inaccurate. Extra Politics is cr
From playlist Extra Politics (ALL EPISODES)
(PP 3.3) Discrete Random Variables
(0:00) Probability mass function (PMF). (4:45) Notation X~blah. (7:10) Examples of discrete random variables: Bernoulli, Binomial, Geometric, Poisson. A playlist of the Probability Primer series is available here: http://www.youtube.com/view_play_list?p=17567A1A3F5DB5E4
From playlist Probability Theory
Discrete versus Continuous Random Variables
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Discrete versus Continuous Random Variables
From playlist Statistics
R - Conditional Inference Trees and Random Forests
Lecturer: Dr. Erin M. Buchanan Summer 2019 https://www.patreon.com/statisticsofdoom This video is part of my human language modeling class. This video covers the more on collocations (words paired together) using conditional inference trees and random forests. Note: these videos are par
From playlist Human Language (ANLY 540)