Linearization and Gaussian
<p>However an important thing to learn about these distributions is that they represent a model. A model is nothing but a function which says if you give me “x” input, i’ll give you “y” output. If a change in x changes y linearly it is linear model or function. If it doesn’t, it’s non linear.</p>
<p>For example:</p>
<p><strong>y= 3x+2</strong>, <strong>y= 3</strong> and <strong>y= 3/4 x+7</strong> are linear, whereas <strong>y= <em>x²</em></strong>, <strong>y= sqrt(x²+y²)</strong>, <strong>y=x⁵</strong> or <strong>y= log(x)</strong> are non linear.</p>
<p>Important: If you pick a linear function from above and generate a 1000 random numbers to replace x one by one for each number, you’ll get a linear plot like the one displayed above. Similarly If you pick a non linear function you’ll get something like below. Where x axis represents the random numbers you have generated to insert as input to the function, and y represents the output of the function.</p>
<p><a href="https://medium.com/@er.ujjwalsaxena/linearization-and-gaussian-700ab40fe900"><strong>Click Here</strong></a></p>