Modeling

/ˌsɪmjʊˈleɪʃən/

noun — "the imitation of a real system over time."

/ˈkɑːrsənz rul/

noun — "a formula to estimate the bandwidth of a frequency-modulated signal."

/flʌks/

noun … “flow that carries change.”

/ˈbuːt.stræp/

noun … “resampling your way to reliability.”

/ɛnˈtrəpi/

noun … “measuring uncertainty in a single number.”

Entropy is a fundamental concept in information theory, probability, and thermodynamics that quantifies the uncertainty, disorder, or information content in a system or random variable. In the context of information theory, introduced by Claude Shannon, entropy measures the average amount of information produced by a stochastic source of data. Higher entropy corresponds to greater unpredictability, while lower entropy indicates more certainty or redundancy.

/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.

/ˈbraʊ.ni.ən ˈmoʊ.ʃən/

noun … “random jittering with a mathematical rhythm.”

/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.

/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.

/ˈmæksɪməm ˈlaɪk.li.hʊd ˌɛstɪˈmeɪʃən/

noun … “finding the parameters that make your data most believable.”