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Una Nueva Sociedad, un nuevo orden de las cosas!
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Rescooped by juandoming from Social Infografic Trend Social Media Metrics & Web Design Strategic Marketing
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Social Media Around the World: A Complete Infographic Guide

Social Media Around the World: A Complete Infographic Guide | Web 2.0 for juandoming | Scoop.it

The world of social media is increasing and has a powerful role to play in the future.

View this infographic to find more statistics about online behavior and the use of Facebook, Twitter, LinkedIn, YouTube, Pinterest and Instagram across the globe...


Via Lauren Moss, Angie Mc, Ivo Nový, luigi vico
Angie Mc's curator insight, January 26, 2014 10:13 AM

Making friends worldwide is one of the best parts of participating in social media.  I especially love how Twitter makes it easy to connect across time zones.

Lydia Gracia's curator insight, February 14, 2014 5:20 AM

La Guia completa del Social Media en el Mundo

Professor Jill Jameson's curator insight, March 4, 2014 10:52 AM

Astonishing figures! 

Rescooped by juandoming from e-Xploration
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#Bayesian Methods for Hackers | #datascience

#Bayesian Methods for Hackers | #datascience | Web 2.0 for juandoming | Scoop.it
Bayesian Methods for Hackers : An intro to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view.

Via luiy
luiy's curator insight, October 14, 2013 11:35 AM

Bayesian Methods for Hackers is designed as a introduction to Bayesian inference from a computational/understanding-first, and mathematics-second, point of view. Of course as an introductory book, we can only leave it at that: an introductory book. For the mathematically trained, they may cure the curiosity this text generates with other texts designed with mathematical analysis in mind. For the enthusiast with less mathematical-background, or one who is not interested in the mathematics but simply the practice of Bayesian methods, this text should be sufficient and entertaining.

 

The choice of PyMC as the probabilistic programming language is two-fold. As of this writing, there is currently no central resource for examples and explanations in the PyMC universe. The official documentation assumes prior knowledge of Bayesian inference and probabilistic programming. We hope this book encourages users at every level to look at PyMC. Secondly, with recent core developments and popularity of the scientific stack in Python, PyMC is likely to become a core component soon enough.

PyMC does have dependencies to run, namely NumPy and (optionally) SciPy. To not limit the user, the examples in this book will rely only on PyMC, NumPy, SciPy and Matplotlib only.

Leonardo Auslender's curator insight, October 15, 2013 7:40 AM

Not at this moment.

 

Rescooped by juandoming from Social Infografic Trend Social Media Metrics & Web Design Strategic Marketing
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Social Media Around the World: A Complete Infographic Guide

Social Media Around the World: A Complete Infographic Guide | Web 2.0 for juandoming | Scoop.it

The world of social media is increasing and has a powerful role to play in the future.

View this infographic to find more statistics about online behavior and the use of Facebook, Twitter, LinkedIn, YouTube, Pinterest and Instagram across the globe...


Via Lauren Moss, Angie Mc, Ivo Nový, luigi vico
Angie Mc's curator insight, January 26, 2014 10:13 AM

Making friends worldwide is one of the best parts of participating in social media.  I especially love how Twitter makes it easy to connect across time zones.

Lydia Gracia's curator insight, February 14, 2014 5:20 AM

La Guia completa del Social Media en el Mundo

Professor Jill Jameson's curator insight, March 4, 2014 10:52 AM

Astonishing figures! 

Scooped by juandoming
Scoop.it!

Bayesian network - Wikipedia, the free encyclopedia

A Bayesian network, Bayes network, belief network, Bayes(ian) model or probabilistic directed acyclic graphical model is a probabilistic graphical model (a type of statistical model) that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.

Formally, Bayesian networks are directed acyclic graphs whose nodes represent random variables in the Bayesian sense: they may be observable quantities, latent variables, unknown parameters or hypotheses. Edges represent conditional dependencies; nodes which are not connected represent variables which are conditionally independent of each other. Each node is associated with a probability function that takes as input a particular set of values for the node's parent variables and gives the probability of the variable represented by the node. For example, if the parents are m Boolean variables then the probability function could be represented by a table of 2^m entries, one entry for each of the 2^m possible combinations of its parents being true or false. Similar ideas may be applied to undirected, and possibly cyclic, graphs; such are called Markov networks.

Efficient algorithms exist that perform inference and learning in Bayesian networks. Bayesian networks that model sequences of variables (e.g. speech signals or protein sequences) are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams.

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