Hello, today I will shortly explain what dynamic time warping (DTW) is and show you my code implementation of it. What is it:Lets say you got 2 signal that are the same. The difference between them though might be in length (one is more streched then the other) or/and in phase (same high of the … Read more
Howdy! Today I will explain another algorithm used within the space of Hidden Markov Models: the Forward Algorithm. Please revise the previous blog articles about HMMs and Markov Chains if you are not familiar with the syntax. Goal of te Forward Algrithm: Its goal is to compute the probability of a sequence of observed variables … Read more
In this post I would like to explain the Viterbi algorithm as simple as possible. It is used to compute (EFFICIENTLY) the most likely sequence of hidden states in a HMM given a sequence of observed variables. The following pictures I drew should explain it. Afterwards you can find a code corresponding to it – … Read more
This post builds on the previous post about Markov chains. Now I will explain what Hiddem Markov Models are and how to calculate the most likely sequence of Markov chain states given a observable variable sequence. Content of this post: What is a HMM HMM explained Calculate the probability of a state sequence given observable … Read more
Hope you are doing well! Today I will explain “Markov Chains” as a prerequisite for a future post. Content of this post: Short description of Markov chains – What are they Detailed explaination of Markov chains – using a weather example How to calculate the state probabilities random walk equilibrium eigenvector approach – with brief … Read more
In this post I would like to introduce the Gradient Descent and its applications. I hope that every reader of this blog is familiar with derivatives and gradients of (simple in school learned) functions, such as f(x)=7x²+3x+9. If you derive the mentioned function, you get the derivative f'(x)=14x+3. However, if you have functions that depend on … Read more
In this post, I write about a simple method of creating a simple linear regression using two formulas. “Simple Linear Regression” is a linear regression model with a singl explanatory variable. A linear regression trys to fit the data with a linear line, hence linear regression. A linear model follows the following equation: Where: m: … Read more
In this post I will shortly explain what Newton’s method is and how it works. What is it: Newton’s method is an algorithm to find zeropoints of a function Because you cannot always calculate the exact zeropoint we can use this algorithm to approximate the zeros of the function How does it work: Start at … Read more
Right away the most important thought in this post: Covariance by itself only gives the direction of correlatedness Without knowing the scales of your variables, you can’t tell which variables are more correlated Imagine we have a sample X filled with weight values of people.In addition we also have another sample Y with height values … Read more
In this post I will explain how to calculate the variance and standard deviation, what they are, explain two common question which are mostly not clarified in school books/by teachers and also give away a Python code to calculate both. To quantify the variation of values to a mean, one can use the so-called “variance” … Read more
