Yozshugrel Normalmente se distribuye con una media de Si lo que estoy sosteniendo es un cachorro, la probabilidad de que sea lindo es muy alto. Primero necesitamos expandir nuestra probabilidad marginal, P cabello largo. Nayesiana manera similar, P hombre con cabello largo es. Por ahora asumimos que esto es una constante, es decir, que la escala es imparcial.
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Individual probabilities can be combined with the techniques of the Markovian discrimination too. Relevant discussion may be found on Talk:Naive Bayes spam filtering.
Please help to ensure that disputed statements are reliably sourced. May Learn how and when to remove this template message One of the main advantages[ citation needed ] of Bayesian spam filtering is that it can be trained on a per-user basis.
For example, a user may have been subscribed to an online newsletter that the user considers to be spam. This online newsletter is likely to contain words that are common to all newsletters, such as the name of the newsletter and its originating email address. The legitimate e-mails a user receives will tend to be different. For example, in a corporate environment, the company name and the names of clients or customers will be mentioned often. The filter will assign a lower spam probability to emails containing those names.
The word probabilities are unique to each user and can evolve over time with corrective training whenever the filter incorrectly classifies an email. As a result, Bayesian spam filtering accuracy after training is often superior to pre-defined rules. It can perform particularly well in avoiding false positives,[ citation needed ] where legitimate email is incorrectly classified as spam. For example, if the email contains the word "Nigeria", which is frequently used in Advance fee fraud spam, a pre-defined rules filter might reject it outright.
A Bayesian filter would mark the word "Nigeria" as a probable spam word, but would take into account other important words that usually indicate legitimate e-mail. For example, the name of a spouse may strongly indicate the e-mail is not spam, which could overcome the use of the word "Nigeria. A spammer practicing Bayesian poisoning will send out emails with large amounts of legitimate text gathered from legitimate news or literary sources.
Words that normally appear in large quantities in spam may also be transformed by spammers. The recipient of the message can still read the changed words, but each of these words is met more rarely by the Bayesian filter, which hinders its learning process.
As a general rule, this spamming technique does not work very well, because the derived words end up recognized by the filter just like the normal ones. The whole text of the message, or some part of it, is replaced with a picture where the same text is "drawn". However, since many mail clients disable the display of linked pictures for security reasons, the spammer sending links to distant pictures might reach fewer targets.
Some filters are more inclined to decide that a message is spam if it has mostly graphical contents. A solution used by Google in its Gmail email system is to perform an OCR Optical Character Recognition on every mid to large size image, analyzing the text inside. It has uses in science, medicine, and engineering. One example is a general purpose classification program called AutoClass which was originally used to classify stars according to spectral characteristics that were otherwise too subtle to notice.
Por su parte, el paradigma Bayesiano se basa en los siguientes postulados: La probabilidad describe grados de creencia, no frecuencias limite. En estos escenarios no estamos considerando la verdadera probabilidad, inherente a la moneda, lo que queremos medir es el grado en que creemos que cada probabilidad puede ocurrir. Con el fin de especificar nuestras creencias debemos medir que tan verosimil pensamos que es cada posible resultado. Por ejemplo, puedes pensar que una mujer mexicana promedio mide cm pero estar abierto a la posibilidad de que el promedio sea un poco mayor o menor. No olvidemos que estamos describiendo probabilidades y por tanto deben cumplir los axiomas de probabilidad.
Teorema de Bayes
Ejercicios Resueltos Prob. Total y Teorema de Bayes