Linear approximation is a method used to estimate the value of a function near a given point using the tangent line at that point. This technique simplifies complex functions into a linear form ...

Formulate linear and integer programming problems for solving commonly encountered optimization problems. Understand how approximation algorithms compute solutions that are guaranteed to be within ...

This tutorial provides practical guidance for researchers seeking to extend DP and RL techniques to larger domains through linear value function approximation. The practical algorithms and empirical ...

Recently, collecting and storing higher order data has become more feasible with the use of methods from multilinear algebra. High order data usually lies in a low dimensional subspace or manifold ...

Latest commit History History 7 lines (5 loc) · 176 Bytes main Breadcrumbs PRML_Tutorial / 3_Linear_Models_for_Regression / 3.5_The_Evidence_Approximation / ...

It will focus on how these methods have been combined with parametric function approximation, including deep learning, to find good approximate solutions to problems that are otherwise too large to be ...

A wide range of empirical applications rely on linear approximations to dynamic Euler equations. Among the most notable of these is the large and growing literature on precautionary saving that ...

How accurate is a log-linear approximation of the New Keynesian model when the nominal interest rate is bounded by zero? This paper compares the solution of the exact non-linear model to the ...