Bayesian inference | Philosophy of mathematics | Bayesian statistics | Probability interpretations
Bayesian epistemology is a formal approach to various topics in epistemology that has its roots in Thomas Bayes' work in the field of probability theory. One advantage of its formal method in contrast to traditional epistemology is that its concepts and theorems can be defined with a high degree of precision. It is based on the idea that beliefs can be interpreted as subjective probabilities. As such, they are subject to the laws of probability theory, which act as the norms of rationality. These norms can be divided into static constraints, governing the rationality of beliefs at any moment, and dynamic constraints, governing how rational agents should change their beliefs upon receiving new evidence. The most characteristic Bayesian expression of these principles is found in the form of Dutch books, which illustrate irrationality in agents through a series of bets that lead to a loss for the agent no matter which of the probabilistic events occurs. Bayesians have applied these fundamental principles to various epistemological topics but Bayesianism does not cover all topics of traditional epistemology. The problem of confirmation in the philosophy of science, for example, can be approached through the Bayesian principle of conditionalization by holding that a piece of evidence confirms a theory if it raises the likelihood that this theory is true. Various proposals have been made to define the concept of coherence in terms of probability, usually in the sense that two propositions cohere if the probability of their conjunction is higher than if they were neutrally related to each other. The Bayesian approach has also been fruitful in the field of social epistemology, for example, concerning the problem of testimony or the problem of group belief. Bayesianism still faces various theoretical objections that have not been fully solved. (Wikipedia).
Bayesian vs frequentist statistics probability - part 1
This video provides an intuitive explanation of the difference between Bayesian and classical frequentist statistics. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm Unfo
From playlist Bayesian statistics: a comprehensive course
A description of the syllabus that will be covered in this course on Bayesian statistics. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm Unfortunately, Ox Educ is no m
From playlist Bayesian statistics: a comprehensive course
What the Heck is Bayesian Stats ?? : Data Science Basics
What's all the hype about Bayesian statistics? My Patreon : https://www.patreon.com/user?u=49277905
From playlist Bayesian Statistics
Bayesian vs frequentist statistics
This video provides an intuitive explanation of the difference between Bayesian and classical frequentist statistics. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm Un
From playlist Bayesian statistics: a comprehensive course
Bayesian vs frequentist statistics probability - part 2
This video provides a short introduction to the similarities and differences between Bayesian and Frequentist views on probability. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdi
From playlist Bayesian statistics: a comprehensive course
An introduction to the use of Bayes' rule in statistics. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm Unfortunately, Ox Educ is no more. Don't fret however as a whol
From playlist Bayesian statistics: a comprehensive course
Statistical Rethinking - Lecture 17
Lecture 17 - Multilevel models (2) - Statistical Rethinking: A Bayesian Course with R Examples
From playlist Statistical Rethinking Winter 2015
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The problem of the origin of the universe and the fundamental nature of reality is a challenging topic. Is there a god, or can the universe have come to exist without one? I had a very mentally stimulating conversation about this with theist Blake Giunta. We talk physics, logic, probabilit
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Statistical Rethinking Fall 2017 - week02 lecture03
Week 02, lecture 03 for Statistical Rethinking: A Bayesian Course with Examples in R and Stan, taught at MPI-EVA in Fall 2017. This lecture covers Chapter 4. Slides are available here: https://speakerdeck.com/rmcelreath Additional information on textbook and R package here: http://xcel
From playlist Statistical Rethinking Fall 2017
Why do Groups Polarize over Matters of Fact? What Models Can and Cannot Tell Us
One striking feature of the current political environment in the United States and elsewhere is that there are large groups with divergent beliefs about matters of fact, including ones where there is a rich and widely accessible body of scientific evidence available that clearly favors one
From playlist Franke Program in Science and the Humanities
Low Default Portfolios (Part 2)
We continue the discussion on LDPs, focusing our attention on the beta-binomial model, and the results of Tasche (2013). The link to the paper is at the end of this description. Which estimator should I use? Answering this question is not easy. From a theoretical point of view, the Bayes
From playlist Topics in Credit Risk Modelling
Statistical Rethinking - Lecture 03
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From playlist Statistical Rethinking Winter 2015
Statistical Rethinking 2022 Lecture 12 - Multilevel Models
Slides and other course materials: https://github.com/rmcelreath/stat_rethinking_2022 Music etc: Intro: https://www.youtube.com/watch?v=HH2UWxnVUyg Wearing: https://www.youtube.com/watch?v=c62C_yTUyVg Pause: https://www.youtube.com/watch?v=wAPCSnAhhC8 Chapters: 00:00 Introduction 07:05 C
From playlist Statistical Rethinking 2022
(ML 7.1) Bayesian inference - A simple example
Illustration of the main idea of Bayesian inference, in the simple case of a univariate Gaussian with a Gaussian prior on the mean (and known variances).
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Statistical Rethinking Winter 2019 Lecture 03
Lecture 03 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 4 of the book.
From playlist Statistical Rethinking Winter 2019
(ML 7.2) Aspects of Bayesian inference
An informal overview of Bayesian inference, Bayesian procedures, Objective versus Subjective Bayes, Pros/Cons of a Bayesian approach, and priors.
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Robert Crease - From MG to QB - G4G14 Apr 2022
Interpretations of quantum mechanics from Martin Gardner to the present.
From playlist G4G14 Videos
(ML 11.8) Bayesian decision theory
Choosing an optimal decision rule under a Bayesian model. An informal discussion of Bayes rules, generalized Bayes rules, and the complete class theorems.
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7 Bayes' rule in inference the prior and denominator
This provides a short introduction into the use of Bayes' rule in inference, by going through an example where the prior and denominator in the formula are explained. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/play
From playlist Bayesian statistics: a comprehensive course
Statistical Rethinking 2023 - 20 - Horoscopes
Course: https://github.com/rmcelreath/stat_rethinking_2023 Music: https://www.youtube.com/watch?v=g2GbpXqL5P8&t=0s Outline 00:00 Introduction 11:40 Planning 30:49 Working 54:41 Pause 55:15 Reporting 1:24:45 Science reform
From playlist Statistical Rethinking 2023