Search results for "Bayesian Modeling"

A Gentle Introduction to Bayesian Belief Networks

A Gentle Introduction to Bayesian Belief Networks

Probabilistic models can define relationships between variables and be used to calculate probabilities. For example, fully conditional models may require an enormous amount of data to cover all possible cases, and probabilities may be intractable to calculate in practice. Simplifying assumptions such as the conditional independence of all random variables can be effective, such as […]

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Example bayesian network

Introduction to Bayesian Networks with Jhonatan de Souza Oliveira

This post is a spotlight interview with Jhonatan de Souza Oliveira on the topic of Bayesian Networks. Could you please introduce yourself? My name is Jhonatan Oliveira and I am an undergraduate student in Electrical Engineering at the Federal University of Vicosa, Brazil. I have been interested in Artificial Intelligence since the beginning of college, when had […]

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Scatter Plot of Dataset With Linear Model and Prediction Interval

Prediction Intervals for Machine Learning

A prediction from a machine learning perspective is a single point that hides the uncertainty of that prediction. Prediction intervals provide a way to quantify and communicate the uncertainty in a prediction. They are different from confidence intervals that instead seek to quantify the uncertainty in a population parameter such as a mean or standard […]

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A Gentle Introduction to Model Selection for Machine Learning

A Gentle Introduction to Model Selection for Machine Learning

Given easy-to-use machine learning libraries like scikit-learn and Keras, it is straightforward to fit many different machine learning models on a given predictive modeling dataset. The challenge of applied machine learning, therefore, becomes how to choose among a range of different models that you can use for your problem. Naively, you might believe that model […]

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A Gentle Introduction to Maximum a Posteriori (MAP) for Machine Learning

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 for short is a Bayesian-based […]

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Histogram of Dataset Constructed From Two Different Gaussian Processes

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. Maximum likelihood becomes intractable if […]

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Probabilistic Model Selection Measures AIC, BIC, and MDL

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 involves using probabilistic statistical measures […]

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A Gentle Introduction to Maximum Likelihood Estimation for Machine Learning

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 for calculating the conditional probability […]

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