Bayesian statistics

Strong prior

In Bayesian statistics, a strong prior is a preceding assumption, theory, concept or idea upon which, after taking account of new information, a current assumption, theory, concept or idea is founded. The term is used to contrast the case of a weak or uninformative prior probability. A strong prior would be a type of informative prior in which the information contained in the prior distribution dominates the information contained in the data being analysed. The Bayesian analysis combines the information contained in the prior with that extracted from the data to produce the posterior distribution which, in the case of a "strong prior", would be little changed from the prior distribution. * v * t * e (Wikipedia).

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Proof by Strong Induction: If x + 1/x is an Integer Then x^n+1/x^n is an Integer

This video provides an example of proof by strong induction. mathispower4u.com

From playlist Sequences (Discrete Math)

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Strong Induction

Strong Induction is a proof method that is a somewhat more general form of normal induction that let's us widen the set of claims we can prove. Our base case is not a single fact, but a list of all the facts up to a particular nth level. Then we demonstrate the (n+1)th level. Previous ex

From playlist Discrete Math (Full Course: Sets, Logic, Proofs, Probability, Graph Theory, etc)

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Why creating a strong password really matters

What is a “strong password”, exactly? What are some ways that you can create a password that is strong and easy to remember?

From playlist Internet Safety

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How to Set up the Null and Alternative Hypothesis

Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys How to Set up the Null and Alternative Hypothesis

From playlist 8.1 Basics of Hypothesis Testing

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Fundamentals of Mathematics - Lecture 12: Strong Ind, Nim, and the Fundamental Theorem of Arithmetic

course page: http://www.uvm.edu/~tdupuy/logic/Math52-Fall2017.html handouts - DZB, Emory videography - Eric Melton, UVM

From playlist Fundamentals of Mathematics

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Proof by Strong Induction [Discrete Math Class]

This video is not like my normal uploads. This is a supplemental video from one of my courses that I made in case students had to quarantine. In this video, we discuss the principle of strong induction: what it is for, why it works, and how to go about using the technique. We compare the t

From playlist Discrete Mathematics Course

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How to Build Your Mental Strength

First, realize there’s a difference between acting tough and actually being mentally strong. Developing mental strength takes practice, and involves overcoming our natural anxieties so we can handle difficult situations. Amy Morin, author of “13 Things Mentally Strong People Don't Do”, s

From playlist Quick Study

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Gaussian approximations in smoothers and filters... - Morzfeld - Workshop 2 - CEB T3 2019

Morzfeld (U Arizona, USA) / 13.11.2019 Gaussian approximations in smoothers and filters for data assimilation ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincar

From playlist 2019 - T3 - The Mathematics of Climate and the Environment

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Statistical Rethinking Winter 2019 Lecture 05

Lecture 05 of the Dec 2018 through March 2019 edition of Statistical Rethinking: A Bayesian Course with R and Stan. This lectures covers the material in Chapter 5 of the book, including multiple regression, intro to causal inference, and categorical variables.

From playlist Statistical Rethinking Winter 2019

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Juan Calderon-Bustillo - Challenge of characterising high-mass compact mergers: the case of GW190521

Recorded 18 November 2021. Juan Calderon-Bustillo of the University of Santiago de Compostela presents "The challenge of characterising high-mass compact mergers: the case of GW190521" at IPAM's Workshop III: Source inference and parameter estimation in Gravitational Wave Astronomy. Abstra

From playlist Workshop: Source inference and parameter estimation in Gravitational Wave Astronomy

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Statistical Rethinking 2023 - 14 - Correlated Features

Course: https://github.com/rmcelreath/stat_rethinking_2023 Music: https://www.youtube.com/watch?v=uf-kTuIfbvM Owl: https://www.youtube.com/watch?v=VNcLbMYwhXQ Pause: https://www.youtube.com/watch?v=pxPdsqrQByM Outline 00:00 Introduction 02:04 Correlated varying effects 12:13 Building the

From playlist Statistical Rethinking 2023

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Matthews Grant - Does God’s Knowledge Quash Free Will?

How can God's perfect knowledge not eliminate free will? Since God-to-be-God can never be wrong and knows everything, including propositions about future events, how can those propositions about future events, which God knows now, not come to pass in the actual future? Hence, if we cannot

From playlist Big Questions About God - Closer To Truth - Core Topic

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Second Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series Talk

Date: Wednesday, October 21, 10:00am EDT Speaker: Chang-Ock Lee, Computational Mathematics and Imaging Lab, Department of Mathematical Sciences, KAIST Title: Artifact suppression in X-ray CT images Abstract: X-ray Computed Tomography (CT) is one of the most powerful techniques for visua

From playlist Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series

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Manuel Rivas: "Large-scale inference across population biobanks"

Computational Genomics Winter Institute 2018 "Large-scale inference across population biobanks" Manuel Rivas, Stanford University Institute for Pure and Applied Mathematics, UCLA March 2, 2018 For more information: http://computationalgenomics.bioinformatics.ucla.edu/programs/2018-cgwi/

From playlist Computational Genomics Winter Institute 2018

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Statistical Rethinking Fall 2017 - week08 lecture15

Week 08, lecture 15 for Statistical Rethinking: A Bayesian Course with Examples in R and Stan, taught at MPI-EVA in Fall 2017. This lecture covers Chapter 12. Slides are available here: https://speakerdeck.com/rmcelreath/statistical-rethinking-fall-2017-lecture-15 Additional informatio

From playlist Statistical Rethinking Fall 2017

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NIPS 2011 Domain Adaptation Workshop: On the utility of unlabeled samples in Domain Adaptation

Domain Adaptation Workshop: Theory and Application at NIPS 2011 Invited Speaker: On the utility of unlabeled samples in Domain Adaptation by Shai Ben-David Shai Ben-David grew up in Jerusalem, Israel and attended the Hebrew University studying physics, mathematics and psychology. He r

From playlist NIPS 2011 Domain Adaptation Workshop

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Constraining Cosmological Parameters with Strongly Lensed Gravitational-Wave Events by Souvik Jana

ICTS In-house 2022 Organizers: Chandramouli, Omkar, Priyadarshi, Tuneer Date and Time: 20th to 22nd April, 2022 Venue: Ramanujan Hall inhouse@icts.res.in An exclusive three-day event to exchange ideas and research topics amongst members of ICTS.

From playlist ICTS In-house 2022

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The Weakness of Strength

When people close to us annoy us, and we wonder why we allowed them into our lives, we should draw vital comfort from a theory known as The Weakness of Strength. If you like our films take a look at our shop (we ship worldwide): http://www.theschooloflife.com/shop/all/ Brought to you by

From playlist SELF

Related pages

Bayesian statistics | Prior probability