estimation. The smoothing problem is closely related to the filtering problem, both of which are studied in Bayesian smoothing theory. A smoother is often a...

In the theory of stochastic processes, filtering describes the problem of determining the state of a system from an incomplete and potentially noisy set...

In machine learning, the term stochastic parrot is a metaphor to describe the claim that large language models, though able to generate plausible language...

exponential smoothing function of the intensity functions at the last time points of event occurrences and outperforms other nine stochastic processes on 8 real-world...

in computational science The Smoothing problem in stochastic processes. See Smoothing problem (stochastic processes) Smooth (disambiguation) Polishing This...

In probability theory and statistics, a Gaussian process is a stochastic process (a collection of random variables indexed by time or space), such that...

dealing with spatial data. It involves stochastic processes (random fields, point processes), sampling, smoothing and interpolation, regional (areal unit)...

Soviet Union Functional integration Operational calculus Smoothing problem (stochastic processes) List of things named after Norbert Wiener A full bibliography...

Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e...

0} (no smoothing), the smoothing spline converges to the interpolating spline. As λ → ∞ {\displaystyle \lambda \to \infty } (infinite smoothing), the roughness...

Exponential smoothing or exponential moving average (EMA) is a rule of thumb technique for smoothing time series data using the exponential window function...

§ Problem-Solving in Egypt and Babylon Brezinski, Meurant & Redivo-Zaglia 2022, p. 34 Tanton 2005, p. 9 Kvasz 2006, p. 290 Corry 2024, § Problem Solving...

most important and central stochastic processes in the theory of stochastic processes. These two processes are Markov processes in continuous time, while...

Unsolved problems in statistics Additive smoothing (also called Laplace smoothing) "LII. An essay towards solving a problem in the doctrine of chances. By the...

available till (including) time  t. Kalman filter Filtering problem (stochastic processes) Errors and residuals in statistics Innovation butterfly C.E...

Dynamic programming is the approach to solve the stochastic optimization problem with stochastic, randomness, and unknown model parameters. It studies...

stochastic analysis (the extension of calculus to stochastic processes) and of differential geometry. The connection between analysis and stochastic processes...

Theodoridis, Sergios (2015-04-10). "Chapter 1. Probability and Stochastic Processes". Machine Learning: A Bayesian and Optimization Perspective. Academic...

is also called "Kalman Smoothing". There are several smoothing algorithms in common use. The Rauch–Tung–Striebel (RTS) smoother is an efficient two-pass...

fundamental data smoothing problem where inferences about the population are made based on a finite data sample. In some fields such as signal processing and econometrics...

Supersymmetric theory of stochastic dynamics (STS) is a multidisciplinary approach to stochastic dynamics on the intersection of dynamical systems theory...

problem by applying Bellman's principle of optimality and then working out backwards in time an optimizing strategy can be generalized to stochastic control...

equation involves some known stochastic processes, for example, the Wiener process in the case of diffusion equations. A stochastic partial differential equation...

problem can be optimized using a stochastic approximation algorithm by using F ( ⋅ , ξ ) = f ξ {\displaystyle F(\cdot ,\xi )=f_{\xi }} . Stochastic variance...

jointly wide sense stationary stochastic processes can be estimated by averaging the product of samples measured from one process and samples measured from...

stochastic processes. The pair ( X t , Y t ) {\displaystyle (X_{t},Y_{t})} is a hidden Markov model if X t {\displaystyle X_{t}} is a Markov process whose...

have many forms and represent different stochastic processes. When modeling variations in the level of a process, three broad classes of practical importance...

Stochastic gradient Langevin dynamics (SGLD) is an optimization and sampling technique composed of characteristics from Stochastic gradient descent, a...

theorem Small area estimation Smearing retransformation Smoothing Smoothing spline Smoothness (probability theory) Snowball sampling Sobel test Social...

Stochastic approximation methods are a family of iterative methods typically used for root-finding problems or for optimization problems. The recursive...