Statistical approximations | Regression analysis
In general, a function approximation problem asks us to select a function among a well-defined class that closely matches ("approximates") a target function in a task-specific way. The need for function approximations arises in many branches of applied mathematics, and computer science in particular, such as predicting the growth of microbes in microbiology. Function approximations are used where theoretical models are unavailable or hard to compute. One can distinguish two major classes of function approximation problems: First, for known target functions approximation theory is the branch of numerical analysis that investigates how certain known functions (for example, special functions) can be approximated by a specific class of functions (for example, polynomials or rational functions) that often have desirable properties (inexpensive computation, continuity, integral and limit values, etc.). Second, the target function, call it g, may be unknown; instead of an explicit formula, only a set of points of the form (x, g(x)) is provided. Depending on the structure of the domain and codomain of g, several techniques for approximating g may be applicable. For example, if g is an operation on the real numbers, techniques of interpolation, extrapolation, regression analysis, and curve fitting can be used. If the codomain (range or target set) of g is a finite set, one is dealing with a classification problem instead. To some extent, the different problems (regression, classification, fitness approximation) have received a unified treatment in statistical learning theory, where they are viewed as supervised learning problems. (Wikipedia).
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Using a polynomial to approximate a function at f(0). More free lessons at: http://www.khanacademy.org/video?v=sy132cgqaiU
From playlist Calculus
Polynomial approximations -- Calculus II
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From playlist Calculus II
Linear Approximations and Differentials
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From playlist Partial Derivatives
Polynomial approximation of functions (part 2)
Approximating a function with a polynomial by making the derivatives equal at f(0) (Maclauren Series) More free lessons at: http://www.khanacademy.org/video?v=3JG3qn7-Sac
From playlist Calculus
Using Taylor Polynomials to Approximate Functions
This video shows how to determine a Taylor Polynomial to approximate a function. http://mathispower4u.yolasite.com/
From playlist Infinite Sequences and Series
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This video gives an informal definition of the limit of a function, and how to start understanding It. The precise definition is given in a later video. For more videos visit http://www.mysecretmathtutor.com
From playlist Calculus
How to find the position function given the acceleration function
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From playlist Riemann Sum Approximation
Evaluate the left and right hand limit of basic ap calculus examples
👉 Learn about the limit of a function. The limit of a function as the input variable of the function tends to a number/value is the number/value which the function approaches at that time. The limit of a function is said to exist if the value which the function approaches as x (or the inde
From playlist Evaluate the Limit..........Help!
Anthony Nouy: Approximation and learning with tree tensor networks - Lecture 2
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From playlist Numerical Analysis and Scientific Computing
Anthony Nouy: "Approximation and learning with tree tensor networks"
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From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021
A nearly optimal lower bound on the approximate degree of AC00- Mark Bun
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From playlist Mathematics
A quick run over different approximation methods an why we would use them. The reference to the Skogestad method is to this video: https://youtu.be/pSG1FBxCvkE
From playlist Laplace
Lecture: Approximation 2018-09-10
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From playlist Lectures
Anthony Nouy: Adaptive low-rank approximations for stochastic and parametric equations [...]
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist Numerical Analysis and Scientific Computing
Accelerating MCMC for Computationally Intensive Models by Natesh Pillai
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From playlist Advances in Applied Probability II (Online)
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From playlist Summer of Math Exposition Youtube Videos
DeepMind x UCL RL Lecture Series - Approximate Dynamic Programming [10/13]
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From playlist Learning resources
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From playlist Calculus Ch 3 - Derivatives