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Los servicios de seguridad que implementados van a permitir contrarrestar las amenazas previamente identificadas, son los siguientes:
a) Confidencialidad de datos.
b) Integridad del mensaje y del contenido.
c) Identificación electrónica: Autenticación de entidades, firma digital.
d) No repudio - acuse de recibo.
e) Control de acceso.

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La planificación no es más que un conjunto de decisiones que toma la organización, representada por su alta dirección, y que responden a las siguientes cuestiones:
- Cuál es la posición futura deseable.
- Cuál es la situación Actual.
- Cuáles son los escalones que necesitamos establecer para pasar de donde estamos a donde queremos llegar.

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BANCO DE DATOS. Básicamente podemos contar con tres acepciones:

1. Estructura de datos donde todos los ficheros están fácilmente disponibles para su uso en un cálculo específico.
2. Archivo de datos sobre un tema recopilado por varias fuentes. Hay: Públicos y privados. Factuales y documentales.
3. Sinónimo de Base de datos, no presenta diferencias con ésta

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DIFERENCIAS ENTRE BASE Y BANCO DE DATOS.
Las diferencias con bancos de datos según las definiciones del apartado segundo:

1. El banco de datos puede tener cualquier estructura de información. Por el contrario la base de datos sólo puede tener alguna de alguna de red, jerárquica y relacional.
2. Base de datos hace referencia a la forma de estructurar la información y no a la fuente de ésta.

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El Diccionario de Recursos de Información (DRI), es un archivo que contiene "metadatos", es decir, datos acerca de los datos (definiciones de datos) y ello porque es necesario guardar información sobre la propia BD. Podemos diferenciar: - Diccionario de datos: agrupa la información (descripción lógica) sobre los datos almacenados en la BD. Depósito de información sobre la definición, estructura y uso de los datos. Esta información es utilizada para análisis, planificación, control y documentación general, a lo largo de la vida del sistema. - Directorio de datos: subsistema del SGBD encargado de describir dónde y cómo se almacenan los datos de la BD. - DRI: diccionario de recursos de información. Se llega a esto concepto por evolución e integración de los anteriores. - Catálogo de un SGBD relacional: Es un DRI implementado como una propia BD relacional. Por tanto, se accede a él igual que a cualquier otra BD de aplicación. Esta BD especial se llama también Metabase. Metabase es una base de datos de una base de datos o metadatos son datos sobre los datos.

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Article 1663932435724

Subject 2. Probability Function
#reading-10-common-probability-distributions

Every random variable is associated with a probability distribution that describes the variable completely. A probability function is one way to view a probability distribution. It specifies the probability that the random variable takes on a specific value; P(X = x) is the probability that a random variable X takes on the value x. A probability function has two key properties: 0 ≤ P(X=x) ≤ 1, because probability is a number between 0 and 1. ΣP(X=x) = 1. The sum of the probabilities P(X=x) over all values of X equals 1. If there is an exhaustive list of the distinct possible outcomes of a random variable and the probabilities of each are added up, the probabilities must sum to 1. The following examples will utilize these two properties in order to examine whether they are probability functions. Example 1 p(x) = x/6 for X = 1, 2, 3, and p(x) = 0 otherwise Substituting into p(x): p(1) = 1/6, p(2) = 2/6 and p(3) = 3/6 Note that it is not necessary to substitute in a



Flashcard 1663933746444

Tags
#reading-10-common-probability-distributions
Question
A [...] is one way to view a probability distribution.
Answer
probability function


statusnot learnedmeasured difficulty37% [default]last interval [days]               
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Subject 2. Probability Function
Every random variable is associated with a probability distribution that describes the variable completely. A probability function is one way to view a probability distribution. It specifies the probability that the random variable takes on a specific value; P(X = x) is the probability that a random variable X takes on the value x. A probability fun







#reading-10-common-probability-distributions
A probability function is one way to view a probability distribution. It specifies the probability that the random variable takes on a specific value; P(X = x) is the probability that a random variable X takes on the value x.

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Subject 2. Probability Function
Every random variable is associated with a probability distribution that describes the variable completely. A probability function is one way to view a probability distribution. It specifies the probability that the random variable takes on a specific value; P(X = x) is the probability that a random variable X takes on the value x. A probability function has two key properties: 0 ≤ P(X=x) ≤ 1, because probability is a number between 0 and 1. ΣP(X=x) = 1. The sum of the pro




Flashcard 1663937678604

Tags
#reading-10-common-probability-distributions
Question
For a discrete random variable, the shorthand notation for a probability function is [...]
Answer
p(x) = P(X = x).


statusnot learnedmeasured difficulty37% [default]last interval [days]               
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Subject 2. Probability Function
ay, is 0 if X is continuous. In a continuous case, only a range of values can be considered (that is, 0 < X < 10), whereas in a discrete case, individual values have positive probabilities associated with them. <span>For a discrete random variable, the shorthand notation is p(x) = P(X = x). For continuous random variables, the probability function is denoted f(x) and called probability density function (pdf), or just the density. This function is effectively the continuous







Flashcard 1663940037900

Tags
#reading-10-common-probability-distributions
Question
For continuous random variables, the probability function is denoted [...]
Answer
f(x)


statusnot learnedmeasured difficulty37% [default]last interval [days]               
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Subject 2. Probability Function
e of values can be considered (that is, 0 < X < 10), whereas in a discrete case, individual values have positive probabilities associated with them. For a discrete random variable, the shorthand notation is p(x) = P(X = x). <span>For continuous random variables, the probability function is denoted f(x) and called probability density function (pdf), or just the density. This function is effectively the continuous analogue of the discrete probability function p(x). The prob







Flashcard 1663942397196

Tags
#reading-10-common-probability-distributions
Question
f(x) and called [...]
Answer
probability density function (pdf)


statusnot learnedmeasured difficulty37% [default]last interval [days]               
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scheduled repetition interval               last repetition or drill
Subject 2. Probability Function
a discrete case, individual values have positive probabilities associated with them. For a discrete random variable, the shorthand notation is p(x) = P(X = x). For continuous random variables, the probability function is denoted <span>f(x) and called probability density function (pdf), or just the density. This function is effectively the continuous analogue of the discrete probability function p(x). The probability density function, which has the symbol







#reading-10-common-probability-distributions
For a discrete random variable, the shorthand notation is p(x) = P(X = x). For continuous random variables, the probability function is denoted f(x) and called probability density function (pdf), or just the density. This function is effectively the continuous analogue of the discrete probability function p(x).

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Subject 2. Probability Function
ay, is 0 if X is continuous. In a continuous case, only a range of values can be considered (that is, 0 < X < 10), whereas in a discrete case, individual values have positive probabilities associated with them. <span>For a discrete random variable, the shorthand notation is p(x) = P(X = x). For continuous random variables, the probability function is denoted f(x) and called probability density function (pdf), or just the density. This function is effectively the continuous analogue of the discrete probability function p(x). The probability density function, which has the symbol f(x), does not give probabilities, despite its name. Instead, it is the area between the graph and the horizontal axi




#reading-10-common-probability-distributions
The probability density function, which has the symbol f(x), does not give probabilities, despite its name. Instead, it is the area between the graph and the horizontal axis that gives probabilities. Because of this, the height of f(x) is not restricted to the range 0 to 1, and the graph, which in itself is not a probability, is unrestricted as far as its height is concerned.

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Subject 2. Probability Function
inuous random variables, the probability function is denoted f(x) and called probability density function (pdf), or just the density. This function is effectively the continuous analogue of the discrete probability function p(x). <span>The probability density function, which has the symbol f(x), does not give probabilities, despite its name. Instead, it is the area between the graph and the horizontal axis that gives probabilities. Because of this, the height of f(x) is not restricted to the range 0 to 1, and the graph, which in itself is not a probability, is unrestricted as far as its height is concerned. From this information, it follows that the area under the entire graph (i.e., between the graph and the x-axis) must equal 1, because this area encapsulates all the pro




#reading-10-common-probability-distributions
From this information, it follows that the area under the entire graph (i.e., between the graph and the x-axis) must equal 1, because this area encapsulates all the probability contained in the random variable. Recall that for discrete distributions, the probabilities add up to 1.

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Subject 2. Probability Function
the horizontal axis that gives probabilities. Because of this, the height of f(x) is not restricted to the range 0 to 1, and the graph, which in itself is not a probability, is unrestricted as far as its height is concerned. <span>From this information, it follows that the area under the entire graph (i.e., between the graph and the x-axis) must equal 1, because this area encapsulates all the probability contained in the random variable. Recall that for discrete distributions, the probabilities add up to 1. Because continuous random variables are concerned with a range of values, individual values have no probabilities, because there is no area associated with individual v




#reading-10-common-probability-distributions
Because continuous random variables are concerned with a range of values, individual values have no probabilities, because there is no area associated with individual values. Rather, probabilities are calculated over a range of values. Another way of saying this is that p(x) = 0 for every individual X.

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Subject 2. Probability Function
e entire graph (i.e., between the graph and the x-axis) must equal 1, because this area encapsulates all the probability contained in the random variable. Recall that for discrete distributions, the probabilities add up to 1. <span>Because continuous random variables are concerned with a range of values, individual values have no probabilities, because there is no area associated with individual values. Rather, probabilities are calculated over a range of values. Another way of saying this is that p(x) = 0 for every individual X. If a discrete random variable has many possible outcomes, then it can be treated as a continuous random variable for conciseness, and ranges of values can be considered




#reading-10-common-probability-distributions
If a discrete random variable has many possible outcomes, then it can be treated as a continuous random variable for conciseness, and ranges of values can be considered in determining probabilities.

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Subject 2. Probability Function
vidual values have no probabilities, because there is no area associated with individual values. Rather, probabilities are calculated over a range of values. Another way of saying this is that p(x) = 0 for every individual X. <span>If a discrete random variable has many possible outcomes, then it can be treated as a continuous random variable for conciseness, and ranges of values can be considered in determining probabilities. <span><body><html>