Hidden_Markov_Model
Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (i.e. hidden) states. Hidden Markov models are especially known for their application in reinforcement learning and temporal pattern recognition such as speech, handwriting, gesture recognition, part-of-speech tagging, musical score following, partial discharges and bioinformatics.
- Cold days are encoded by a 0 and hot days are encoded by a 1.
- The first day in our sequence has an 80% chance of being cold.
- A cold day has a 30% chance of being followed by a hot day.
- A hot day has a 20% chance of being followed by a cold day.
- On each day the temperature is normally distributed with mean and standard deviation 0 and 5 on a cold day and mean and standard deviation 15 and 10 on a hot day.
- Tensorflow
- Tensorflow_probability