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## Tag: Probability

A Gentle Introduction to Computational Learning Theory
Computational learning theory, or statistical learning theory, refers to mathematical frameworks for quantifying learning tasks and algorithms. These are sub-fields of machine learning that a machine learning practitioner does not need to know in great depth in order to achieve good results on a wide range of problems. Nevertheless, it …
New Statistics Course: Conditional Probability in R
Learn the fundamentals of conditional probability in R with this interactive statistics course. Master Naive Bayes and learn to build a spam filter with R! The post New Statistics Course: Conditional Probability in R appeared first on Dataquest.
Develop an Intuition for Bayes Theorem With Worked Examples
Bayes Theorem provides a principled way for calculating a conditional probability. It is a deceptively simple calculation, providing a method that is easy to use for scenarios where our intuition often fails. The best way to develop an intuition for Bayes Theorem is to think about the meaning of the …
A Gentle Introduction to the Bayes Optimal Classifier
The Bayes Optimal Classifier is a probabilistic model that makes the most probable prediction for a new example. It is described using the Bayes Theorem that provides a principled way for calculating a conditional probability. It is also closely related to the Maximum a Posteriori: a probabilistic framework referred to …
Looking for similar NBA games, based on win probability time series
Inpredictable, a sports analytics site by Michael Beuoy, tracks win probabilities of NBA…Tags: basketball, probability
How to Use an Empirical Distribution Function in Python
An empirical distribution function provides a way to model and sample cumulative probabilities for a data sample that does not fit a standard probability distribution. As such, it is sometimes called the empirical cumulative distribution function, or ECDF for short. In this tutorial, you will discover the empirical probability distribution …
Free Probability Textbook
Introduction to Probability by Joseph Blitzstein and Jessica Hwang is available as a free PDF download. The book contains: Distributions Random Variables Markov Chains Monte Carlo All the background Math Code Examples in R and lots more….
A Gentle Introduction to Stochastic in Machine Learning
The behavior and performance of many machine learning algorithms are referred to as stochastic. Stochastic refers to a variable process where the outcome involves some randomness and has some uncertainty. It is a mathematical term and is closely related to “randomness” and “probabilistic” and can be contrasted to the idea …
A Gentle Introduction to Maximum a Posteriori (MAP) for Machine Learning
Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. Typically, estimating the entire distribution is intractable, and instead, we are happy to have the expected value of the distribution, such as the mean or mode. Maximum a Posteriori or MAP …
A Gentle Introduction to Markov Chain Monte Carlo for Probability
Probabilistic inference involves estimating an expected value or density using a probabilistic model. Often, directly inferring values is not tractable with probabilistic models, and instead, approximation methods must be used. Markov Chain Monte Carlo sampling provides a class of algorithms for systematic random sampling from high-dimensional probability distributions. Unlike Monte …
A Gentle Introduction to Monte Carlo Sampling for Probability
Monte Carlo methods are a class of techniques for randomly sampling a probability distribution. There are many problem domains where describing or estimating the probability distribution is relatively straightforward, but calculating a desired quantity is intractable. This may be due to many reasons, such as the stochastic nature of the …
A Gentle Introduction to Expectation Maximization (EM Algorithm)
Maximum likelihood estimation is an approach to density estimation for a dataset by searching across probability distributions and their parameters. It is a general and effective approach that underlies many machine learning algorithms, although it requires that the training dataset is complete, e.g. all relevant interacting random variables are present. …
New Python Statistics Course: Conditional Probability
Learn about conditional probability for data science, including Bayes' Theorem and Naive Bayes algorithms, in this new interactive online course. The post New Python Statistics Course: Conditional Probability appeared first on Dataquest.
Probabilistic Model Selection with AIC, BIC, and MDL
Model selection is the problem of choosing one from among a set of candidate models. It is common to choose a model that performs the best on a hold-out test dataset or to estimate model performance using a resampling technique, such as k-fold cross-validation. An alternative approach to model selection …
A Gentle Introduction to Logistic Regression With Maximum Likelihood Estimation
Logistic regression is a model for binary classification predictive modeling. The parameters of a logistic regression model can be estimated by the probabilistic framework called maximum likelihood estimation. Under this framework, a probability distribution for the target variable (class label) must be assumed and then a likelihood function defined that …
A Gentle Introduction to Linear Regression With Maximum Likelihood Estimation
Linear regression is a classical model for predicting a numerical quantity. The parameters of a linear regression model can be estimated using a least squares procedure or by a maximum likelihood estimation procedure. Maximum likelihood estimation is a probabilistic framework for automatically finding the probability distribution and parameters that best …
A Gentle Introduction to Maximum Likelihood Estimation for Machine Learning
Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. There are many techniques for solving density estimation, although a common framework used throughout the field of machine learning is maximum likelihood estimation. Maximum likelihood estimation involves defining a likelihood function …
A Gentle Introduction to Cross-Entropy for Machine Learning
Cross-entropy is commonly used in machine learning as a loss function. Cross-entropy is a measure from the field of information theory, building upon entropy and generally calculating the difference between two probability distributions. It is closely related to but is different from KL divergence that calculates the relative entropy between …