500+ quant interview questions for Jane Street, Citadel, Two Sigma, DE Shaw, and other top quantitative finance firms.
Statistical analysis and quantitative modeling problems
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Difficulty: Hard
Category: machine_learning
Practice quant interview questions from top firms including Jane Street, Citadel, Two Sigma, DE Shaw, and other leading quantitative finance companies.
Topics: hmm, viterbi, regime_detection, dynamic_programming, machine_learning
Hidden Markov Models (HMMs) are used in quantitative finance to model systems with unobserved states, such as bull and bear market regimes. The Viterbi algorithm provides an efficient dynamic programming solution to decode the most likely sequence of these hidden states from a series of observed data, like asset returns. This process, known as Viterbi decoding, is crucial for regime detection and tactical asset allocation strategies. Task Implement the function hmm_viterbi(observations, pi, A,
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