Bayesian statistics | Deductive reasoning | Probability assessment

An a priori probability is a probability that is derived purely by deductive reasoning. One way of deriving a priori probabilities is the principle of indifference, which has the character of saying that, if there are N mutually exclusive and collectively exhaustive events and if they are equally likely, then the probability of a given event occurring is 1/N. Similarly the probability of one of a given collection of K events is K / N. One disadvantage of defining probabilities in the above way is that it applies only to finite collections of events. In Bayesian inference, "uninformative priors" or "objective priors" are particular choices of a priori probabilities.Note that "prior probability" is a broader concept. Similar to the distinction in philosophy between a priori and a posteriori, in Bayesian inference a priori denotes general knowledge about the data distribution before making an inference, while a posteriori denotes knowledge that incorporates the results of making an inference. (Wikipedia).

Learn to find the or probability from a tree diagram

đź‘‰ 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

Expected Value of a Discrete Probability Distribution

This video explains how to determine the expected value or mean value of a discrete probability distribution. http://mathispower4u.com

From playlist Probability

Ex: Determine Conditional Probability from a Table

This video provides two examples of how to determine conditional probability using information given in a table.

From playlist Probability

Ex: Determine Probability Given Odds

This video explains how to determine probability given odds.

From playlist Probability

Expected Value of a Binomial Probability Distribution

Today, we derive the formula to find the expected value or the mean of a discrete random variable which follows the binomial probability distribution.

From playlist Probability

How to find the probability of consecutive events

đź‘‰ 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

(New Version Available) Conditional Probability

New Version: Fixes an error at 7:00: https://youtu.be/WgsxhWPAo4c This video explains how to determine conditional probability. http://mathispower4u.yolasite.com/

From playlist Counting and Probability

Determining the conditional probability from a contingency table

đź‘‰ 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

Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Introduction to Probability

From playlist Statistics

Optimal State Estimator Algorithm | Understanding Kalman Filters, Part 4

Download our Kalman Filter Virtual Lab to practice linear and extended Kalman filter design of a pendulum system with interactive exercises and animations in MATLAB and Simulink: https://bit.ly/3g5AwyS Discover the set of equations you need to implement a Kalman filter algorithm. Youâ€™ll l

From playlist Understanding Kalman Filters

Lecture 1 | Modern Physics: Statistical Mechanics

March 30, 2009 - Leonard Susskind discusses the study of statistical analysis as calculating the probability of things subject to the constraints of a conserved quantity. Susskind introduces energy, entropy, temperature, and phase states as they relate directly to statistical mechanics.

From playlist Lecture Collection | Modern Physics: Statistical Mechanics

Oxford 4b The Argument Concerning Induction

A course by Peter Millican from Oxford University. Course Description: Dr Peter Millican gives a series of lectures looking at Scottish 18th Century Philosopher David Hume and the first book of his Treatise of Human Nature. Taken from: https://podcasts.ox.ac.uk/series/introduction-david

From playlist Oxford: Introduction to David Hume's Treatise of Human Nature Book One | CosmoLearning Philosophy

Probabilistic inverse problems (Lecture 1) by Daniela Calvetti

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)

21. Hypothesis Testing and Random Walks

MIT 6.262 Discrete Stochastic Processes, Spring 2011 View the complete course: http://ocw.mit.edu/6-262S11 Instructor: Robert Gallager License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

From playlist MIT 6.262 Discrete Stochastic Processes, Spring 2011

Christian Jutten - Petite visite guidĂ©e de la sĂ©paration de sources

GIPSA-Lab, Prix Science et Innovation 2016 RĂ©alisation technique : Antoine Orlandi (GRICAD) | Tous droits rĂ©servĂ©s

From playlist Des mathĂ©maticiens primĂ©s par l'AcadĂ©mie des Sciences 2017

Mikhail Lyubich: Story of the Feigenbaum point

HYBRID EVENT Recorded during the meeting "Advancing Bridges in Complex Dynamics" the September 23, 2021 by the Centre International de Rencontres MathĂ©matiques (Marseille, France) Filmmaker: Luca RĂ©canzone Find this video and other talks given by worldwide mathematicians on CIRM's Audi

From playlist Dynamical Systems and Ordinary Differential Equations

5 Idealisms & their Refutations - Kant's Critique of Pure Reason (Dan Robinson)

Dan Robinson gives the 5th lecture in a series of 8 on Immanuel Kant's Critique of Pure Reason. All 8 lectures: https://www.youtube.com/playlist?list=PLhP9EhPApKE_OdgqNgL0AJX9-gwr4tmLw The very possibility of self-awareness (an "inner sense" with content) requires an awareness of an exte

From playlist Kant's Critique of Pure Reason - Dan Robinson

How To Tackle Our Inverse Problem by Wolfgang Waltenberger

Discussion Meeting : Hunting SUSY @ HL-LHC (ONLINE) ORGANIZERS : Satyaki Bhattacharya (SINP, India), Rohini Godbole (IISc, India), Kajari Majumdar (TIFR, India), Prolay Mal (NISER-Bhubaneswar, India), Seema Sharma (IISER-Pune, India), Ritesh K. Singh (IISER-Kolkata, India) and Sanjay Kuma

From playlist HUNTING SUSY @ HL-LHC (ONLINE) 2021

Shannon 100 - 26/10/2016 - Elisabeth GASSIAT

Entropie, compression et statistique Elisabeth Gassiat (UniversitĂ© de Paris-Sud) Claude Shannon est l'inventeur de la thĂ©orie de l'information. Il a introduit la notion d'entropie comme mesure de l'information contenue dans un message vu comme provenant d'une source stochastique et dĂ©mon

From playlist Shannon 100

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