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P-values and confidence intervals? Think again
P-values and confidence intervals? Think again

On Generative Parallel Composition
On Generative Parallel Composition

Children`s understanding of probability
Children`s understanding of probability

- Philsci
- Philsci

Week 3 - Seminar
Week 3 - Seminar

... Conditional Probability Conditional Probability  The probability of an event occurring, given that another event has already occurred  Denoted P(B | A) (read “probability of B, given A”) For Example:  There are 6 frosted donuts and 6 plain donuts in a box. If you select one at random, what’s the ...
General Database Statistics Using Entropy Maximization
General Database Statistics Using Entropy Maximization

Programming Language Techniques for Cryptographic Proofs⋆
Programming Language Techniques for Cryptographic Proofs⋆

Spike train entropy-rate estimation using hierarchical Dirichlet
Spike train entropy-rate estimation using hierarchical Dirichlet

Spike train entropy-rate estimation using
Spike train entropy-rate estimation using

INDUCTIVE LEARNING IN SMALL AND LARGE WORLDS 1
INDUCTIVE LEARNING IN SMALL AND LARGE WORLDS 1

Working Paper Series Default Times, Non-Arbitrage
Working Paper Series Default Times, Non-Arbitrage

... As Kusuoka pointed out, the above example does not fulfill the immersion property. It is natural to investigate if such a model is arbitrage-free. Let us assume that Θ(F, P) is not empty, i.e., the τ -default-free market is arbitrage free, and let us introduce the following alternative non arbitrage ...
Propagation of errors Propagation of errors
Propagation of errors Propagation of errors

Uniqueness of maximal entropy measure on essential
Uniqueness of maximal entropy measure on essential

... [1, 8]. Thus, conditioned on FG\H , all configurations FH that contain paths joining distinct infinite trees of FG\H have probability 0. This example also shows, perhaps surprisingly, that µ ∈ EG does not imply that, conditioned on FG\H , all extensions of FG\H to an element of Ω are equally likely. ...
(pdf)
(pdf)

... Though they did not report any numbers in their paper, our calculations show that their analysis of LP decoding allows (3, 6)-regular codes to tolerate random error rate 0.01 —a factor of 5 improvement over Daskalakis et al. [7]. In this paper we present an improvement of the error rate by another f ...
Estimating Subjective Probabilities
Estimating Subjective Probabilities

MCMCRev.pdf
MCMCRev.pdf

A Guided Tour of Sets, Functions, and Random Variables
A Guided Tour of Sets, Functions, and Random Variables

PowerPoint - ECSE - Rensselaer Polytechnic Institute
PowerPoint - ECSE - Rensselaer Polytechnic Institute

x - Royal Holloway
x - Royal Holloway

... extent that the fluctuation of the sum is not dominated by one (or few) terms. Beware of measurement errors with non-Gaussian tails. Good example: velocity component vx of air molecules. OK example: total deflection due to multiple Coulomb scattering. ...
Using Probability and Probability Distributions
Using Probability and Probability Distributions

CHAPTER 1
CHAPTER 1

Optimal Information Transmission
Optimal Information Transmission

Bounding Bloat in Genetic Programming
Bounding Bloat in Genetic Programming

statistical covariance as a measure of phylogenetic relationship
statistical covariance as a measure of phylogenetic relationship

Connectivity Properties of Random Subgraphs of the Cube - IME-USP
Connectivity Properties of Random Subgraphs of the Cube - IME-USP

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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. Only inference establishes new facts from data.The basis of inference is Bayes' theorem. But this theorem is sometimes hard to apply and understand. The simpler method to understand inference is in terms of quantities of information.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.
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