Philosophy of statistics | Probability theory | Inference

Inductive probability

Inductive probability attempts to give the probability of future events based on past events. It is the basis for inductive reasoning, and gives the mathematical basis for learning and the perception of patterns. It is a source of knowledge about the world. There are three sources of knowledge: inference, communication, and deduction. Communication relays information found using other methods. Deduction establishes new facts based on existing facts. Inference establishes new facts from data. Its basis is Bayes' theorem. Information describing the world is written in a language. For example, a simple mathematical language of propositions may be chosen. Sentences may be written down in this language as strings of characters. But in the computer it is possible to encode these sentences as strings of bits (1s and 0s). Then the language may be encoded so that the most commonly used sentences are the shortest. This internal language implicitly represents probabilities of statements. Occam's razor says the "simplest theory, consistent with the data is most likely to be correct". The "simplest theory" is interpreted as the representation of the theory written in this internal language. The theory with the shortest encoding in this internal language is most likely to be correct. (Wikipedia).

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Learn to find the or probability from a tree diagram

πŸ‘‰ 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

πŸ‘‰ 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 two way frequency table

πŸ‘‰ 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 conditional probability from a contingency table

πŸ‘‰ 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

πŸ‘‰ 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 contingency table to find the conditional probability

πŸ‘‰ 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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Determining the conditional probability from a contingency table

πŸ‘‰ 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 conditional probability from a tree diagram

πŸ‘‰ 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

πŸ‘‰ 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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HSC Science Extension Module 1 Induction and Deduction

HSC Science Extension Module 1 Foundations of Scientific Thinking Induction and Deduction

From playlist Y12 Sci Ex Mod 1 Foundations of Scientific Thinking

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Mathematical induction -- Proofs

This lecture is on Introduction to Higher Mathematics (Proofs). For more see http://calculus123.com.

From playlist Proofs

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Transcience for the interchange process in dimension 5 - Allan Sly

Probability Seminar Topic: Transcience for the interchange process in dimension 5 Speaker: Allan Sly Affiliation: Princeton University Date: October 07, 2022Β  The interchange process \sigma_T is a random permutation valued process on a graph evolving in time by transpositions on its edge

From playlist Mathematics

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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

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CS25 I Stanford Seminar - Transformer Circuits, Induction Heads, In-Context Learning

"Neural network parameters can be thought of as compiled computer programs. Somehow, they encode sophisticated algorithms, capable of things no human knows how to write a computer program to do. Mechanistic interpretability seeks to reverse engineer neuralΒ networks into human understandabl

From playlist Stanford Seminars

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How to Argue - Induction & Abduction: Crash Course Philosophy #3

We continue our look at philosophical reasoning by introducing two more types: induction and abduction. Hank explains their strengths and weaknesses, as well as counterarguments and the Socratic method. -- Images and video via VideoBlocks or Wikimedia Commons, licensed under Creative Co

From playlist Philosophy

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Univalent Foundations of Mathematics - Vladimir Voevodsky

Univalent Foundations of Mathematics - Vladimir Voevodsky Institute for Advanced Study December 10, 2010 The correspondence between homotopy types and higher categorical analogs of groupoids which was first conjectured by Alexander Grothendieck naturally leads to a view of mathematics wh

From playlist Mathematics

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02 Bayesian evidential learning

Introduction to Bayesianism

From playlist QUSS GS 260

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1. Ch. 1 (Part 1/3) Introduction to Logic, Philosophy 10, UC San Diego - BSLIF

Video lecture corresponding to _Basic Sentential Logic and Informal Fallacies_, Introduction, and Chapter 1, Part 1 of 3. This is for the class Introduction to Logic, Philosophy 10, UC San Diego.

From playlist UC San Diego: PHIL 10 - Introduction to Logic | CosmoLearning.org Philosophy

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How to create a tree diagram from a word problem

πŸ‘‰ 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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Inequality Mathematical Induction Proof: 2^n greater than n^2

In this video I give a proof by induction to show that 2^n is greater than n^2. Proofs with inequalities and induction take a lot of effort to learn and are very confusing for people who are new to induction. I really hope this video helps someone!

From playlist Principle of Mathematical Induction

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Bayes' theorem | Frequentist probability | Law of total probability | Andrey Kolmogorov | Ray Solomonoff | Outcome (probability) | Probability | Algorithmic probability | Pareto efficiency | Prior probability | Solomonoff's theory of inductive inference | Event (probability theory) | Decision theory | Occam's razor | Algorithmic information theory | Information theory | Expression (mathematics) | Huffman coding | Inference | Artificial intelligence | Kolmogorov complexity | Partition of a set | Conditional probability | Overfitting | Probability distribution | Combinatorial explosion | Inductive reasoning | Probability measure | Halting problem | Minimum message length | Thomas Bayes | Possible world | Principle of indifference | Calculus | Minimum description length | Quantifier (logic) | Isaac Newton | Experiment (probability theory) | Boolean algebra | Jorma Rissanen | Bayesian inference | Abductive reasoning | Frequentist inference | Inductive logic programming