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### Probability Distributions in Data Science - KDnuggets

Bio: Pier Paolo Ippolito is a final year MSc Artificial Intelligence student at The University of Southampton. He is an AI Enthusiast, Data Scientist and RPA Developer.

### What is a Probability Distribution ? Determine its Type for Your Data

Probability Distribution is an important topic that each data scientist should know for the analysis of the data. It defines all the related possibility outcomes of a variable. In this, the article you will understand all the Probability Distribution types that help you to determine the distribution for the dataset. There are two types of distribution. In the discrete Distribution, the sum of the probabilities of all the individuals is equal to one.

### Probability Density and Mass Functions in Machine Learning - Machine Philosopher

You will hear the term probability distribution many times when working with data and machine learning models. These are extremely helpful in certain cases such as naive Bayes' where the model needs to know a lot about the probabilities of its data! What it will be referring to is either the probability density function or the probability mass function of our data, lets have a look at the important differences! In machine learning, we often provide models with distributions of probabilities to tell us about what values any new data samples are likely to be. If we are working with continuous random variables, then we would use a probability density function to model the probability of any variable being near a certain value (continuous data does not have exact probabilities, as we will see below).

### Markov Chain Monte Carlo with People

Many formal models of cognition implicitly use subjective probability distributions to capture the assumptions of human learners.

### An Approximation of Surprise Index as a Measure of Confidence

Probabilistic graphical models, such as Bayesian networks, are intuitive and theoretically sound tools for modeling uncertainty. A major problem with applying Bayesian networks in practice is that it is hard to judge whether a model fits well a case that it is supposed to solve. One way of expressing a possible dissonance between a model and a case is the {\em surprise index}, proposed by Habbema, which expresses the degree of surprise by the evidence given the model. While this measure reflects the intuition that the probability of a case should be judged in the context of a model, it is computationally intractable. In this paper, we propose an efficient way of approximating the surprise index.