Markov Model

Current state only dependent on previous state

Markov property is quite reasonable since physical reality also changes based on current state

However, the reason hidden states are necessary is because we need to understand trends in changes that cannot be predicted by current stock prices alone in order to incorporate them into the model parameters

The Markov assumption is a concept proposed by Russian mathematician Markov around 1913 to build a model for the sequence of letters in Russian literature

Markov Model Notion

Markov Process

Markov Property

Markov Random Field

Markov model

In probability theory, a Markov model is a stochastic model used to model pseudo-randomly changing systems. It is assumed that future states depend only on the current state, not on the events that occurred before it (that is, it assumes the Markov property). Generally, this assumption enables reasoning and computation with the model that would otherwise be intractable. For this reason, in the fields of predictive modelling and probabilistic forecasting, it is desirable for a given model to exhibit the Markov property.

https://en.wikipedia.org/wiki/Markov_model

Markov Model

Current state only dependent on previous state

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