In a hidden markov model, the markov chain is hidden, we can only observe outcome values. Therefore, additional concepts such as observed states and emission probabilities are explained.
Markov chains are a succession of random events. Using the example of chutes and ladders, concepts such as states, initial probability, and transition matrix are explained.
Examines the relationship between tuberculosis cases, mortality, resistance, etc. compared to country's GDP, population, etc. Using this data a variety of statistical tests and models are formed for prediction.
Visualization of the relationship between tuberculosis cases, mortality, resistance, etc. compared to country's GDP, population, etc. Tidyverse is used for data cleaning while ggplot2 was used for graphing.