Monte Carlo methods : Monte Carlo methods are a class of computational algorithms that use random sampling to solve mathematical problems. These methods are named after the city of Monte Carlo in Monaco, where the first random sampling experiments were conducted in the early 20th century. One example of a Monte Carlo method is the […]

Monte Carlo maximum likelihood (MCML) : Monte Carlo maximum likelihood (MCML) is a computational method used to estimate the maximum likelihood of a given set of parameters, given a set of observed data. This method uses random sampling to generate a large number of potential parameter sets, and then uses the likelihood function to evaluate […]

Monotonic sequence : A monotonic sequence is a sequence in which the terms either strictly increase or strictly decrease. This means that the terms of the sequence either consistently get larger or consistently get smaller as the sequence progresses. One example of a monotonic sequence is the sequence of natural numbers: 1, 2, 3, 4, […]

Monotonic regression : Monotonic regression is a type of regression analysis that involves finding a non-decreasing or non-increasing relationship between a dependent variable and an independent variable. This means that the dependent variable will either always increase or always decrease as the independent variable increases. One example of monotonic regression is a study on the […]

Mojena’s test : Mojena’s test is a statistical method used to evaluate the performance of a clustering algorithm. The test is named after its creator, Italian statistician Antonio Mojena, who proposed it in a paper published in 1952. The idea behind Mojena’s test is to measure the degree to which a clustering algorithm is able […]

Model Monitoring : Model monitoring is the process of continuously evaluating and assessing the performance and accuracy of a machine learning model over time. This is an important step in the development and deployment of any machine learning system, as it allows for the identification and correction of any potential issues or biases that may […]

Model Evaluation : Model evaluation is the process of assessing the performance of a model on a given dataset. This is important because it helps determine if a model is suitable for a particular task or if it needs to be improved. There are various methods for evaluating models, and each method has its own […]

Model Drift : Model drift is a phenomenon in which the performance of a machine learning model deteriorates over time due to changes in the distribution of the data on which it was trained. This can happen for a variety of reasons, such as changes in the underlying business environment or shifts in consumer behavior. […]

Model building : Model building is the process of creating a mathematical representation of a real-world system or phenomenon. This representation is used to make predictions, understand relationships, and identify patterns in the data. There are many different types of models, each with its own strengths and limitations. Here are two examples of model building: […]

Model-based inference : Model-based inference is a statistical approach that involves the use of mathematical models to make predictions or inferences about a given set of data. This method is often used in fields such as economics, engineering, and biology to analyze complex systems and make predictions based on the data. One example of model-based […]