Statistical data types

Realization (probability)

In probability and statistics, a realization, observation, or observed value, of a random variable is the value that is actually observed (what actually happened). The random variable itself is the process dictating how the observation comes about. Statistical quantities computed from realizations without deploying a statistical model are often called "empirical", as in empirical distribution function or empirical probability. Conventionally, to avoid confusion, upper case letters denote random variables; the corresponding lower case letters denote their realizations. (Wikipedia).

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Probability - Quantum and Classical

The Law of Large Numbers and the Central Limit Theorem. Probability explained with easy to understand 3D animations. Correction: Statement at 13:00 should say "very close" to 50%.

From playlist Physics

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Learn to find the or probability from a tree diagram

👉 Learn how to find the conditional probability of an event. Probability is the chance of an event occurring or not occurring. The probability of an event is given by the number of outcomes divided by the total possible outcomes. Conditional probability is the chance of an event occurring

From playlist Probability

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How to find the probability of consecutive events

👉 Learn how to find the conditional probability of an event. Probability is the chance of an event occurring or not occurring. The probability of an event is given by the number of outcomes divided by the total possible outcomes. Conditional probability is the chance of an event occurring

From playlist Probability

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Introduction to Probability

This video introduces probability and determine the probability of basic events. http://mathispower4u.yolasite.com/

From playlist Counting and Probability

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Finding the conditional probability from a two way frequency table

👉 Learn how to find the conditional probability of an event. Probability is the chance of an event occurring or not occurring. The probability of an event is given by the number of outcomes divided by the total possible outcomes. Conditional probability is the chance of an event occurring

From playlist Probability

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How to find the theoretical probability of choosing a number

👉 Learn how to find the theoretical probability of an event. Probability is the chance of an event occurring or not occurring. The probability of an event is given theoretically by the number of outcomes divided by the total possible outcomes. Conditional probability questions can come in

From playlist Probability

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How to create a tree diagram from a word problem

👉 Learn how to find the conditional probability of an event. Probability is the chance of an event occurring or not occurring. The probability of an event is given by the number of outcomes divided by the total possible outcomes. Conditional probability is the chance of an event occurring

From playlist Probability

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Using a tree diagram to find the conditional probability

👉 Learn how to find the conditional probability of an event. Probability is the chance of an event occurring or not occurring. The probability of an event is given by the number of outcomes divided by the total possible outcomes. Conditional probability is the chance of an event occurring

From playlist Probability

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Finding the conditional probability from a tree diagram

👉 Learn how to find the conditional probability of an event. Probability is the chance of an event occurring or not occurring. The probability of an event is given by the number of outcomes divided by the total possible outcomes. Conditional probability is the chance of an event occurring

From playlist Probability

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Lec 20 | MIT 6.451 Principles of Digital Communication II, Spring 2005

The Sum-Product Algorithm View the complete course: http://ocw.mit.edu/6-451S05 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

From playlist MIT 6.451 Principles of Digital Communication II

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Foundations for Learning in the Age of Big Data II - Maria Florina Balcan

Topic: Foundations for Learning in the Age of Big Data Speaker: Maria Florina Balcan Affiliation: Carnegie Mellon University Date: May 24, 2022 Balcan-2022-05-24

From playlist Mathematics

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20e Spatial Data Analytics: Summarizing Uncertainty

Subsurface modeling course lecture on summarizing uncertainty.

From playlist Spatial Data Analytics and Modeling

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16c Data Analytics: Decision Making

Lecture on decision making in the presence of uncertainty. Follow along with the demonstration workflow in Python: o. Decision making, optimum estimation in the presence of uncertainty: https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/Interactive_DecisionMaking.ipynb Foll

From playlist Data Analytics and Geostatistics

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SGEMS SGSIM

Introduction to Sequential Gaussian Simulation in SGEMS

From playlist SGEMS tutorial

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Jere Koskela: Inference for coalescent and diffusion models in genetics (3/3)

Abstract: Mathematical models in population genetics frequently come in pairs: a diffusion process describes the forward-in-time evolution of allele frequencies in a population, and a branching-coalescing particle system describes the random genetic ancestry of a sample on sequences from t

From playlist Summer School on Stochastic modelling in the life sciences

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Gregory Margulis - The Abel Prize interview 2020

00:00 congratulations to Gregory Margulis 01:33 when did you interests in mathematics start? 02:33 growing up in Moscow in the 50’s and 60’s and being included in mathematical circles 05:47 mathematical Olympiads 06:32 early career and the paper with Kazhdan 08:03 Margulis at the Institute

From playlist Gregory Margulis

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Bootstrap and Monte Carlo

Bootstrap and Monte Carlo Teacher: Dr. Michael Pyrcz For more webinars & events please checkout: http://daytum.io/events Website: https://www.daytum.io/ Twitter: https://twitter.com/daytum_io?lang=en LinkedIn: https://www.linkedin.com/company/35593451 Data Science Education for Energy P

From playlist daytum Free Webinar Series

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Probabilistic inverse problems (Lecture - 2) by Erkki Somersalo

DISCUSSION MEETING WORKSHOP ON INVERSE PROBLEMS AND RELATED TOPICS (ONLINE) ORGANIZERS: Rakesh (University of Delaware, USA) and Venkateswaran P Krishnan (TIFR-CAM, India) DATE: 25 October 2021 to 29 October 2021 VENUE: Online This week-long program will consist of several lectures by

From playlist Workshop on Inverse Problems and Related Topics (Online)

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Random Walks (Lecture - 02) by Abhishek Dhar

Bangalore School on Statistical Physics - VIII DATE: 28 June 2017 to 14 July 2017 VENUE: Ramanujan Lecture Hall, ICTS, Bengaluru This advanced level school is the eighth in the series. This is a pedagogical school, aimed at bridging the gap between masters-level courses and topics in s

From playlist Bangalore School on Statistical Physics - VIII

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Probability: "At Least" Probabilities

This is the fourth video of a series from the Worldwide Center of Mathematics explaining the basics of probability. This video deals with calculating probabilities of "at least 1" or other "at least" probability calculations. For more math videos, visit our channel or go to www.centerofmat

From playlist Basics: Probability and Statistics

Related pages

Map (mathematics) | Random variable | Sample space | Raw data | Empirical distribution function | Event (probability theory) | Function (mathematics) | Probability theory | Statistics | Outcome (probability) | Probability | Empirical probability | Elementary event | Subset | Errors and residuals | Random variate