# MÉMOIRE SUR LES PROBABILITÉS∗

@inproceedings{Laplace2010MEMOIRESL, title={MÉMOIRE SUR LES PROBABILITÉS∗}, author={Pierre-Simon de Laplace}, year={2010} }

I intend to treat in this Memoir two important points in the analysis of chances which do not seem yet to have been sufficiently deeply studied: the first has for object the manner of calculating the probability of events composed of simple events of which one does not know the respective probabilities; the object of the second is the influence of past events on the probability of future events, and the law according to which, in its expansion, shows us the causes which have produced them…

## 16 Citations

Journ@l Electronique d'Histoire des Probabilités et de la Statistique Electronic Journ@l for History of Probability and Statistics

- History
- 2011

We examine Poisson’s personal contribution to the probability calculus, placing it in the mathematical and social context of the beginning of the 19th century (§1). Then we look briefly at Poisson’s…

Predicting the Unpredictable

- Mathematics
- 2019

A major difficulty for currently existing theories of inductive inference involves the question of what to do when novel, unknown, or previously unsuspected phenomena occur. In this paper one…

La dispersion des mesures dmographiques : vue historique

- History
- 2010

This paper traces the development of the notions of dispersion, in demography and in probability, distinguishing two main meanings for this term: the one first, to scatter, to cast here and there; in…

Why Gaussianity?

- Computer ScienceIEEE Signal Processing Magazine
- 2008

The main contribution to the topic is concerned with highlighting the role of Gaussian models in signal processing based on the optimal property of the Gaussian distribution minimizing Fisher information over the class of distributions with a bounded variance.

Dispersion of measurements in demography: a historical view

- History
- 2010

This paper traces the development of the notions of dispersion, in demography and in probability, distinguishing two main meanings for this term: the one first, to scatter, to cast here and there; in…

Bayesian Demography 250 Years after Bayes Short title : Bayesian Demography

- Computer Science
- 2015

The aim of this paper is to review the achievements of Bayesian demography, address some misconceptions, and make the case for wider use ofBayesian methods in population studies, focusing on three applications: demographic forecasts, limited data, and highly-structured or complex models.

The Practical Scope of the Central Limit Theorem

- Mathematics
- 2021

The Central Limit Theorem (CLT) is at the heart of a great deal of applied problemsolving in statistics and data science, but the theorem is silent on an important implementation issue: how much data…

KANDINSKY Patterns as IQ-Test for Machine Learning

- Computer ScienceCD-MAKE
- 2019

The results of the study show that the majority of human explanations was made based on the properties of individual elements in an image and the appearance of individual objects and the location of objects played almost no role in the explanation of the images.

Likelihood Prediction for Generalized Linear Mixed Models under Covariate Uncertainty

- Mathematics
- 2014

This article presents the techniques of likelihood prediction for the generalized linear mixed models. Methods of likelihood prediction are explained through a series of examples; from a classical…

Unreliable evidence in binary classification problems

- Computer Science
- 2019

This report explains the problem ofinary classification problems and explores options for accounting for the often-neglected uncertainty and concludes that a neat solution that does no harm to less uncertain cases remains elusive.