What does HMM stand for in the context of continuous observations?
Hidden Markov Model
HMM is used to model systems with hidden states and observable outputs.
What type of output probability density function is used in Continuous HMM?
Gaussian output pdf
This allows for modeling continuous observations in HMM.
What are the key components of the HMM training process?
- Model initialisation
- Expectation Maximisation
- Unsupervised training
These steps are crucial for effectively training a Hidden Markov Model.
What is the purpose of Expectation Maximisation in HMM training?
To optimize the parameters of the model
It iteratively improves the estimates of the hidden states and model parameters.
True or false: Continuous HMM only deals with discrete observations.
FALSE
Continuous HMM is designed to handle continuous observations that take floating-point values.
What is the output probability for a discrete HMM represented as?
B = {bi(k)} = {P(ot = k|xt = i)}
This notation defines the probabilities of observing a specific output given a hidden state.
What graphical model notation is used to represent conditional dependencies in probabilistic methods?
Graphical model notation
This notation visually depicts the relationships between variables in a probabilistic model.
What is the significance of Gaussian pdf in the context of continuous observations?
It models the distribution of continuous data
Gaussian pdf is essential for representing continuous outputs in HMM.
What does the term continuous observations refer to in HMM?
Observations that may take floating-point values
Continuous observations are essential for modeling real-world data that is not discrete.
