Probability represents the certainty factor. Certainty is the rate that you would assign to an event to happen. Say, you

The latter phenomenon is called Bayesian probability. Rather than considering the frequency with which an event repeats, we quantify our belief. Consider the statement — there’s a 32% chance that a diabetic patient is going to develop heart failure. This statement isn’t prone to repetition where we create infinite replicas of the patient’s symptoms. We instead quantify with a 32% certainty that heart failure could happen.

Not all scenarios are frequency related as in our previous assumption. If we consider a machine learning problem in which we estimate the probability of inflation or deflation of the price of fuel, we wouldn’t be thinking this in the perspective of repetition, as seen in the frequentist probability scenario. Instead, we say that this event could occur with a certain probability/certainty.

Formulating an easy and uncertain rule is better in comparison to formulating a complex and certain rule — it’s cheaper to generate and analyze. Moreover, a “certain” rule doesn’t guarantee generating the right and required output always. An “uncertain” rule, on the other hand, though non-deterministic, helps in reaching a generalized conclusion.

If we want to define the probability distribution only on a subset of variables, we use marginal probability distribution. This is useful if we want to estimate the probability on only a specific set of input variables (concerning x attribute) when given the other input values (concerning y attribute).

Covariance defines the linear relation between two variables. If it’s positive, both the variables tend to take higher values and if it’s negative, when one variable takes a higher value, the other takes a lower value.

There are cases where we want to compute the probability of an event when a different event happens. This probability distribution is termed as conditional probability distribution.

Altogether, probability measures the extent of certainty pertaining to an uncertain event. As machine learning revolves around probable yet not mandatory situations, probability plays a crucial role in approximating the analysis.

A discrete variable takes a finite set of values whereas a continuous variable takes an infinite number of values. If you have a dataset with two attributes — age group and profession, age group is continuous and profession is discrete. In probability, we define the probability of discrete variables using probability mass function (PMF).

Variance defines how the output of an event varies as values (influencing the event) are picked from a probability distribution. It defines how the one value differs from the other values, or in simple terms, the variability of the dataset.

If we want to determine the probability distribution on two or more random variables, we use joint probability distribution. For a typical data attribute in machine learning, we have multiple possible values. Computing probability of all values falls under joint probability.

http://go.acaps.cat/kgr/v-ideos-universidad-de-concepcion-v-palestino-v-es-cl-1wvj-20.php

http://live07.colomboserboli.com/niy/Video-alcoyano-v-huesca-v-es-es-1buf-18.php

http://streaming7.actiup.com/kdx/Video-girona-v-lugo-v-es-es-1wbc-25.php

http://streaming7.actiup.com/kdx/v-ideos-girona-v-lugo-v-es-es-1egv-6.php

http://live07.colomboserboli.com/niy/Video-alcoyano-v-huesca-v-es-es-1vme-6.php

http://go.acaps.cat/kgr/v-ideos-universidad-de-concepcion-v-palestino-v-es-cl-1rvt-9.php

http://go.acaps.cat/kgr/videos-universidad-de-concepcion-v-palestino-v-es-cl-1cjr-17.php

http://streaming7.actiup.com/kdx/video-girona-v-lugo-v-es-es-1ldz-29.php

http://live07.colomboserboli.com/niy/video-alcoyano-v-huesca-v-es-es-1nah-23.php

http://main.dentisalut.com/mqk/videos-amorebieta-v-sporting-gijon-v-es-es-1vtu-20.php

http://go.acaps.cat/kgr/video-Girona-Lugo-v-en-gb-1uzh-.php

http://streaming7.actiup.com/kdx/Video-girona-v-lugo-v-es-es-1ufp-11.php

http://go.acaps.cat/kgr/video-Girona-Lugo-v-en-gb-1bsp30122020-14.php

http://main.dentisalut.com/mqk/Video-amorebieta-v-sporting-gijon-v-es-es-1xvs-20.php

http://live07.colomboserboli.com/niy/videos-alcoyano-v-huesca-v-es-es-1tae-22.php

http://streaming7.actiup.com/kdx/Video-Alcoyano-Huesca-v-en-gb-1xib30122020-.php

http://live07.colomboserboli.com/niy/Video-Sporting-Braga-Maritimo-v-en-gb-1zwr30122020-.php

http://go.acaps.cat/kgr/videos-Girona-Lugo-v-en-gb-1mtp-12.php

http://live07.colomboserboli.com/niy/Video-Sporting-Braga-Maritimo-v-en-gb-1qlj-23.php

http://main.dentisalut.com/mqk/v-ideos-amorebieta-v-sporting-gijon-v-es-es-1awz-4.php

resource that has helped me learn skills and neat tricks that I never thought was possible. It’s something that I leveraged significantly while job searching and it is a resource that I will always go back to. The best part of it is that they normally have recommended solutions and discussion boards, so you can learn about more efficient solutions and techniques.

PMF assigns probability to every possible variable specific to the data attribute. The probability for all possible variables shouldn’t exceed 1, as to how certainty shouldn’t exceed 100%. Each probability concerning a variable has to lie between (included) 0 and 1. If we consider the profession attribute, we can define its probability by stating that every profession’s probability has to be in between 0 and 1, and all of them have to add up to 1.

Probability distribution defines the likelihood of possible values that a random variable can take. PMF and PDF that have been described earlier for discrete and continuous variables respectively are probability distributions.

The probability of continuous variables can be defined using probability density function (PDF). As continuous variables are not finite, we use an integral to define PDF. The probability of every possible continuous value has to be greater than or equal to zero but not preferably less than or equal to 1 as a continuous value isn’t finite. Although, the integration of all probabilities has to be equal to 1.

The frequentist probability denotes the frequency with which the event can happen amongst many trials/events. Rolling a dice is frequentist as ⅙ means that out of infinitely many trials of rolling a dice, there’s a 1/6th chance that 6 is going to show up.