隠れマルコフモデル（HMM）
隠れた マルコフモデル (HMM) is a powerful statistical tool used in various fields, including 人工知能, 音声認識, and bioinformatics. It is particularly useful for modeling systems that exhibit a sequence of observable events influenced by internal states that are not directly visible (hence ‘hidden’).
At its core, an HMM consists of two main components: a set of hidden states and a set of observable events. The model assumes that the system transitions between these hidden states according to certain probabilities, and each 隠れ状態 特定の放出確率に基づいて観測可能なイベントを生成します。
HMMの主な特徴は次のとおりです：
- 状態： The underlying states of the system, which are not directly observable but can be inferred from the 観測データ.
- 観測： The events or outputs that can be seen and measured, which provide clues about the hidden states.
- 遷移確率： The probabilities of moving from one hidden state to another, which inform how the system evolves over time.
- 放出確率： 特定の隠れた状態に基づいて特定のイベントを観測する確率。
HMMs are commonly trained using algorithms such as the Baum-Welch algorithm or the Viterbi algorithm, which help estimate the model parameters and find the most likely sequence of hidden states given the observed data. Applications of HMMs span across various domains, including 自然言語処理, where they help in part-of-speech tagging, and in finance for modeling stock prices.