## What is Markov regression?

Markov-Switching Regression Models. Models for time series that transition over a set of finite states. States are unobserved and the process can switch among states throughout the sample. The time of transition between states and the duration in a particular state are both random.

## What is switching regression?

A switching regression model is used to either classify unobservable states or to estimate the transition probabilities for these unobservable states in a time series. A simple time series is for instance is the price of gold on the stock market.

What are hidden Markov models used for?

A hidden Markov model (HMM) is a statistical model that can be used to describe the evolution of observable events that depend on internal factors, which are not directly observable.

What is endogenous switching regression model?

Endogenous switching regression models are natural extensions of classical experimental designs, which allow tests of assumptions about the exogeneity of treatment effects from survey data. Switching regression models for continuous variables can be generalized to account for binary and censored dependent variables.

### Why is hidden Markov model Memoryless?

Just that it goes to a new state every time just based on the present, and never by looking at the past. And when it changes state, it says (outputs) some information to the world. So where it goes and what it says is purely based on the present, making it memoryless.

### What is hidden Markov model in machine learning?

A Hidden Markov Model (HMM) is a statistical model which is also used in machine learning. It can be used to describe the evolution of observable events that depend on internal factors, which are not directly observable.

Is a hidden Markov model a neural network?

Hidden Markov model (HMM) has been successfully used for sequential data modeling problems. In the proposed GenHMM, each HMM hidden state is associated with a neural network based generative model that has tractability of exact likelihood and provides efficient likelihood computation.