Hidden Markov Model

/ˈhɪd.ən ˈmɑːrkɒv ˈmɒd.əl/

noun … “seeing the invisible through observable clues.”

Hidden Markov Model (HMM) is a statistical model that represents systems where the true state is not directly observable but can be inferred through a sequence of observed emissions. It extends the concept of a Markov Process by introducing hidden states and probabilistic observation models, making it a cornerstone in temporal pattern recognition tasks such as speech recognition, bioinformatics, natural language processing, and gesture modeling.

Markov Process

/ˈmɑːr.kɒv ˈprəʊ.ses/

noun … “the future depends only on the present, not the past.”

Markov Process is a stochastic process in which the probability of transitioning to a future state depends solely on the current state, independent of the sequence of past states. This “memoryless” property, known as the Markov property, makes Markov Processes a fundamental tool for modeling sequential phenomena in probability, statistics, and machine learning, including Hidden Markov Models, reinforcement learning, and time-series analysis.

Naive Bayes

/naɪˈiːv ˈbeɪz/

noun … “probabilities, simplified and fast.”

Naive Bayes is a probabilistic machine learning algorithm based on Bayes’ theorem that assumes conditional independence between features given the class label. Despite this “naive” assumption, it performs remarkably well for classification tasks, particularly in text analysis, spam detection, sentiment analysis, and document categorization. The algorithm calculates the posterior probability of each class given the observed features and assigns the class with the highest probability.

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