•  #### Hands-On Markov Models with Python #### Overview of this book

Hidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone. Once you’ve covered the basic concepts of Markov chains, you’ll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you’ll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you’ll explore the Bayesian approach of inference and learn how to apply it in HMMs. In further chapters, you’ll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You’ll also learn to apply HMM to image processing using 2D-HMM to segment images. Finally, you’ll understand how to apply HMM for reinforcement learning (RL) with the help of Q-Learning, and use this technique for single-stock and multi-stock algorithmic trading. By the end of this book, you will have grasped how to build your own Markov and hidden Markov models on complex datasets in order to apply them to projects.
Preface  Free Chapter
Introduction to the Markov Process Hidden Markov Models State Inference - Predicting the States Parameter Learning Using Maximum Likelihood Parameter Inference Using the Bayesian Approach Time Series Predicting Natural Language Processing 2D HMM for Image Processing Markov Decision Process Other Books You May Enjoy # Maximum likelihood learning

Before diving into learning about maximum likelihood estimation (MLE) in HMMs, let's try to understand the basic concepts of MLE in generic cases. As the name suggests, MLE tries to select the parameters of the model that maximizes the likelihood of observed data. The likelihood for any model with given parameters is defined as the probability of getting the observed data, and can be written as follows: Here, D={D1, D2, D3, …, Dn} is the observed data, and θ is the set of parameters governing our model. In most cases, for simplicity, we assume that the datapoints are independent and identically distributed (IID). With that assumption, we can simplify the definition of our likelihood function as follows: Here, we have used the multiplication rule for independent random variables to decompose the joint distribution into product...