Showing posts with label How Mathematics formula makes. Show all posts
Showing posts with label How Mathematics formula makes. Show all posts

Tuesday, November 6, 2018

How Mathematics formula makes

How Mathematics formula makes a ball

mathematics,formula,formulas,mathematics formula,maths formulas,important mathematics formulas,memorize math formulas,maths,algebra formulas,math,algebra formulas tricks,how to memorize geometry formulas,how to use mathematical induction to prove a formula,maths formula,math formula,maths formula pdf,math act formulas,important formulas of algebra,algebraic formula,easy formula,school maths formula,maths fomulas

X = 2*rand(n,3)-1;
v = sum(X.^2,2);
>> delta = 0.05;
d = -1:delta:1;
[x0,y0,z0] = meshgrid(d,d,d);
X0 = [x0(:), y0(:), z0(:)];
v0 = griddatan(X,v,X0);
v0 = reshape(v0, size(x0));
>> p = patch(isosurface(x0,y0,z0,v0,0.6));
lighting phong
axis equal
title('Interpolated sphere from scattered data')
regularization,bayesian,machine learning,bayesian statistics,additive regularization,bayesian network applications,deep learning,bayesian deep learning,dropout as a bayesian approximation,bayesian network example solution,bayesian networks tutorial,bayesian inference,bayesian belief network in data mining,bayesian network probability calculation,bayesian networks python,bayesian deep learning tensorflow

Black-Scholes formula-R

 Black-Scholes formula-R > BlackScholes <- function(TypeFlag = c("c", "p"), S, X, Time, r, b, sigma) { TypeFla...