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  2. Picard–Lindelöf theorem - Wikipedia

    en.wikipedia.org/wiki/Picard–Lindelöf_theorem

    In mathematics, specifically the study of differential equations, the Picard–Lindelöf theorem gives a set of conditions under which an initial value problem has a unique solution. It is also known as Picard's existence theorem , the Cauchy–Lipschitz theorem , or the existence and uniqueness theorem .

  3. Continuous uniform distribution - Wikipedia

    en.wikipedia.org/wiki/Continuous_uniform...

    Any probability density function integrates to , so the probability density function of the continuous uniform distribution is graphically portrayed as a rectangle where ⁠ ⁠ is the base length and ⁠ ⁠ is the height. As the base length increases, the height (the density at any particular value within the distribution boundaries) decreases.

  4. Partial differential equation - Wikipedia

    en.wikipedia.org/wiki/Partial_differential_equation

    In the method of separation of variables, one reduces a PDE to a PDE in fewer variables, which is an ordinary differential equation if in one variable – these are in turn easier to solve. This is possible for simple PDEs, which are called separable partial differential equations , and the domain is generally a rectangle (a product of intervals).

  5. Chegg - Wikipedia

    en.wikipedia.org/wiki/Chegg

    Chegg began trading shares publicly on the New York Stock Exchange in November 2013. [15] Its IPO was reported to have raised $187.5 million, with an initial market capitalization of about $1.1 billion. [16] In 2014, Chegg entered a partnership with book distributor Ingram Content Group to distribute all of Chegg's physical textbook rentals ...

  6. Latin hypercube sampling - Wikipedia

    en.wikipedia.org/wiki/Latin_hypercube_sampling

    When sampling a function of variables, the range of each variable is divided into equally probable intervals. sample points are then placed to satisfy the Latin hypercube requirements; this forces the number of divisions, , to be equal for each variable. This sampling scheme does not require more samples for more dimensions (variables); this ...

  7. Least squares - Wikipedia

    en.wikipedia.org/wiki/Least_squares

    The result of fitting a set of data points with a quadratic function Conic fitting a set of points using least-squares approximation. In regression analysis, least squares is a parameter estimation method based on minimizing the sum of the squares of the residuals (a residual being the difference between an observed value and the fitted value provided by a model) made in the results of each ...

  8. Analytic geometry - Wikipedia

    en.wikipedia.org/wiki/Analytic_geometry

    In a manner analogous to the way lines in a two-dimensional space are described using a point-slope form for their equations, planes in a three dimensional space have a natural description using a point in the plane and a vector orthogonal to it (the normal vector) to indicate its "inclination".

  9. Free variables and bound variables - Wikipedia

    en.wikipedia.org/wiki/Free_variables_and_bound...

    Note: we define a location in an expression as a leaf node in the syntax tree. Variable binding occurs when that location is below the node n. In the lambda calculus, x is a bound variable in the term M = λx. T and a free variable in the term T. We say x is bound in M and free in T. If T contains a subterm λx. U then x is rebound in this term.