13.10.08
12.10.08
10.10.08
La formación de la excusa
Se dice que el ser humano utiliza el 10% de su capacidad. Investiguemos cómo nacen las excusas. “Un grupo de científicos ubicó a cinco monos en una jaula, en cuyo centro colocaron una escalera y, sobre ella, un cesto con bananas. Cuando un mono subía, los científicos lanzaban agua helada a los demás. Si algún mono intentaba subir, los otros lo atacaban. Pasado algún tiempo, ningún mono se animaba, por el recuerdo de la experiencia. Entonces, los científicos sustituyeron un mono. Lo primero que hizo fue subir la escalera, siendo rápidamente bajado por los otros. Un segundo mono fue sustituido y ocurrió lo mismo. El primer sustituto participó con entusiasmo de la paliza. Un tercero fue cambiado y se repitió el hecho. El cuarto y el último de los veteranos fueron sustituidos. Quedó un grupo de cinco monos que no recibieron baños de agua fría, y que sin embargo continuaban golpeando al que intentaba llegar a las bananas. Si fuese posible preguntarles por qué, la respuesta sería: no sé, las cosas siempre se han hecho así aquí”.
9.10.08
8.10.08
DirtySexyMoney
Dirty Sexy Money is an American television series about Nick George, portrayed by Peter Krause. George's whole life has been lived in the shadow of the Darling family, but as an adult he's leading the perfect life as an idealistic lawyer, until his father's suspicious death. The wealthy Darlings of New York have asked him to take over his father's job as their personal lawyer, but the money that will allow him the freedom to be an altruistic do-gooder is only part of the picture. That same money pulls him into the dubious doings of the Darling clan.
Principal characters
Peter Krause as Nick George
Zoe McLellan as Lisa George
Donald Sutherland as Tripp Darling
Jill Clayburgh as Letitia Darling
William Baldwin as Patrick Darling
Natalie Zea as Karen Darling
Seth Gabel as Jeremy Darling
Samaire Armstrong as Juliet Darling
Glenn Fitzgerald as Brian Darling Sr.
Blair Underwood as Simon Elder
Lucy Liu as Nola Lyons
6.10.08
Enamorado solo...
...para una vez que me gusta alguien realmente, la suerte no me acompaña...me remito a la frase de un amigo: Dios es el más cínico de todos!
5.10.08
New Master: Stochastic process
A stochastic process, or sometimes random process, is the counterpart to a deterministic process (or deterministic system) in probability theory. Instead of dealing with only one possible 'reality' of how the process might evolve under time (as is the case, for example, for solutions of an ordinary differential equation), in a stochastic or random process there is some indeterminacy in its future evolution described by probability distributions. This means that even if the initial condition (or starting point) is known, there are many possibilities the process might go to, but some paths are more probable and others less.
In the simplest possible case ('discrete time'), a stochastic process amounts to a sequence of random variables known as a time series (for example, see Markov chain). Another basic type of a stochastic process is a random field, whose domain is a region of space, in other words, a random function whose arguments are drawn from a range of continuously changing values. One approach to stochastic processes treats them as functions of one or several deterministic arguments ('inputs', in most cases regarded as 'time') whose values ('outputs') are random variables: non-deterministic (single) quantities which have certain probability distributions. Random variables corresponding to various times (or points, in the case of random fields) may be completely different. The main requirement is that these different random quantities all have the same 'type'.[1] Although the random values of a stochastic process at different times may be independent random variables, in most commonly considered situations they exhibit complicated statistical correlations.
Familiar examples of processes modeled as stochastic time series include stock market and exchange rate fluctuations, signals such as speech, audio and video, medical data such as a patient's EKG, EEG, blood pressure or temperature, and random movement such as Brownian motion or random walks. Examples of random fields include static images, random terrain (landscapes), or composition variations of an inhomogeneous material.
In the simplest possible case ('discrete time'), a stochastic process amounts to a sequence of random variables known as a time series (for example, see Markov chain). Another basic type of a stochastic process is a random field, whose domain is a region of space, in other words, a random function whose arguments are drawn from a range of continuously changing values. One approach to stochastic processes treats them as functions of one or several deterministic arguments ('inputs', in most cases regarded as 'time') whose values ('outputs') are random variables: non-deterministic (single) quantities which have certain probability distributions. Random variables corresponding to various times (or points, in the case of random fields) may be completely different. The main requirement is that these different random quantities all have the same 'type'.[1] Although the random values of a stochastic process at different times may be independent random variables, in most commonly considered situations they exhibit complicated statistical correlations.
Familiar examples of processes modeled as stochastic time series include stock market and exchange rate fluctuations, signals such as speech, audio and video, medical data such as a patient's EKG, EEG, blood pressure or temperature, and random movement such as Brownian motion or random walks. Examples of random fields include static images, random terrain (landscapes), or composition variations of an inhomogeneous material.
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