Concept

Gaussian process

Related lectures (31)

General Linear Processes: Wold Decomposition Theorem

Explores general linear processes, the Wold decomposition theorem, and spectral analysis in time series analysis.

Gaussian Process Regression: Probabilistic Linear Regression

Explores Probabilistic Linear Regression and Gaussian Process Regression, emphasizing kernel selection and hyperparameter tuning for accurate predictions.

Modeling Extremal Processes

Explores extremal limit theorems, Gaussian processes, and modeling rare events in spatial problems and time series.

Max-Stable Models: Smith and Schlather

Covers the Smith and Schlather max-stable models, exploring their validity and interpretation.

Long Memory and ARCH: Time Series Math 342

Explores long memory in time series and Autoregressive Conditional Heteroskedasticity processes in financial data.

Machine Learning for On-Top Pair Density

Delves into using machine learning to predict spinless on-top pair density and understand electron correlation in complex systems.

Point Processes: Convergence and Gaussian Processes

Covers point processes, convergence criteria, Laplace functionals, Gaussian processes, covariance functions, and intrinsic stationarity.

Gaussian Process Regression: Kernels and Comparisons

Explores Gaussian Process Regression kernels, computational costs, and comparisons with Ridge Regression and other non-linear regression techniques.

Sub- and Supermartingales: Theory and Applications

Explores sub- and supermartingales, stopping times, and their applications in stochastic processes.

Vector Autoregression (VAR): Sampling Properties and Examples

Covers Vector Autoregression (VAR) in time series analysis, including sampling properties and examples of VAR processes.

Gaussian Random Vectors: Conditional Generation

Explores generating Gaussian random vectors with specific components based on observed values and explains the concept of positive definite covariance functions in Gaussian processes.

Time Series: Fundamentals and Models

Explores the fundamentals of time series analysis, including stationarity, linear processes, forecasting, and practical aspects.

Graphical Model Learning: M-Estimator Examples

Explores graphical model learning with M-estimators, Gaussian process regression, Google PageRank modeling, density estimation, and generalized linear models.

Fourier Transform: Properties and Applications

Covers the properties and applications of the Fourier transform and its relation to Heisenberg's uncertainty principle.

Martingales and Brownian Motion: Drift and Exit Time

Explores Brownian motion with drift, exit probabilities, and average exit times from intervals.

Stochastic Processes: Brownian Motion

Explores Brownian motion, Langevin equations, and stochastic processes in physics.

Stochastic Processes: Spectral Density, Markov Chains, and Gaussian Variables

Covers spectral density, Markov chains, Gaussian variables, and more in stochastic processes.

Stochastic Processes: Generation and Embedding

Explores the generation of stochastic processes, including Gaussian processes, Markov processes, Poisson processes, and circulant embedding.

Elements of Statistics: Memorylessness and Stationary Processes

Covers memorylessness, stationary processes, MLE estimation, and random walks.

Stochastic Processes: Review and Properties

Covers the review of random variables, probability density functions, variance, and Gaussian processes.