The Nadaraya-Watson kernel regression, named after its developers E. A. Nadaraya and G. A. Watson, is a non-parametric statistical technique used for estimating the conditional expectation of a random variable given another variable. At its core, the Nadaraya-Watson method utilises …
In traditional logistic regression, the focus is on estimating the coefficients of the model that best describe the relationship between the independent variables and the probability of a binary event. On the other hand, Bayesian logistic regression generates entire probability …
Bayesian Logistic Regression with Laplace Approximation More details
In Bayesian statistics, the goal is to update our beliefs about a parameter or set of parameters based on observed data. The posterior distribution represents our updated beliefs, but in many cases, this distribution may be analytically intractable due to …
Distribution approximation with Laplace Approximation More details
The Newton-Raphson method, also known as the Newton method, is a powerful numerical technique used for finding the roots of real-valued functions. It provides an efficient and quick approach to approximate solutions of equations, particularly when analytical methods are impractical. …
Unlike generative models that explicitly model the joint distribution of the input data and class labels, probabilistic discriminative models focus on directly estimating the conditional probability of class labels given the input data. An example of these models is logistic …
Probabilistic Discriminative Modelling with Logistic Regression More details
A probabilistic generative model is a type of statistical model that aims to capture the joint distribution of both input data and their corresponding class labels. In contrast to discriminative models that focus on learning the decision boundary between different …
Fisher’s Linear Discriminant (FLD), also known as Fisher’s Discriminant Analysis, is a statistical technique developed by Sir Ronald A. Fisher in the early 1930s. The main objective of this algorithm is to find a linear combination of features that maximises …
Bayesian Linear Regression is a statistical modeling technique that extends traditional linear regression by incorporating Bayesian principles. In standard linear regression, we aim to model the relationship between independent variables and a dependent variable, assuming fixed coefficients. However, Bayesian Linear …
In the realm of statistical modeling, Linear Regression stands as a stalwart method, serving as the foundation for predictive analytics across various disciplines. Widely embraced for its simplicity and interpretability, the method has become a cornerstone in understanding relationships between …
Gaussian Processes (GPs) are a powerful and flexible tool in the realm of machine learning and probabilistic modeling. Unlike traditional parametric models that have a fixed number of parameters (linear regression, for example), Gaussian Processes define a distribution over functions, …
