Time Series Modelling and State Space Models: Professor Chris Williams, University of Edinburgh
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- Опубликовано: 23 мар 2017
- - AR, MA and ARMA models
- Parameter estimation for ARMA models
- Hidden Markov Models (definitions, inference, learning)
- Linear-Gaussian HMMs (Kalman filtering)
- More advanced topics (more elaborate state-space models, and recurrent neural networks)
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great explanations. like three touch downs in a single game
I agree.. it is a master piece
Where can get the class presentation pdf file?
What is the meaning of Xnj at 29:58? Xn at state j?
I think it's an indicator variable which is 1 when X was j at nth step. To interpret this, compare this to standard markov chain. To get emission probability of j in state i, we'll count all the j's emitted when in state j divided by all observations in state i. Now when the states are hidden, we take all the observations j, and weight them by probability of being in state i at that time step, then normalize the sum by dividing by the sum of weights to get a probability. Hope that clarifies.
too confused !
Rule of lectures: try to impart one or two takeaway concepts. 90 minutes of sprawl ranging from first principles to current research breaks the rule.
he has many take away, it is very logical and gave great intuition. Your altitude is the problem
This lecture is highly dry as there is no effort put in to explain the intuition behind all the math
he gave many intuition especially in the beginning 10-15 mins