This project implements a decoding algorithm using MCMC (Markov Chain Monte Carlo) methods in R. The approach leverages probabilistic sampling to estimate hidden states in a sequence, commonly used in applications like hidden Markov models and Bayesian inference. The code includes data preprocessing, model setup, and result.