A Markov chain implementation in Go.
➜ polo git:(master) ✗ go run .
P(Sunny|Sunny) = 0.80
P(Rainy|Sunny) = 0.05
P(Cloudy|Sunny) = 0.15
P(Sunny|Rainy) = 0.20
P(Rainy|Rainy) = 0.60
P(Cloudy|Rainy) = 0.20
P(Sunny|Cloudy) = 0.20
P(Rainy|Cloudy) = 0.30
P(Cloudy|Cloudy) = 0.50
The probability of tomorrow being Sunny, given that today was Rainy is 0.2
The next state, given that today was Sunny is Sunny
- Basic Markov Chain
- Fix
Chain.Next()
- Implement
cumsum
and implement betterrand.Sample
algorithm - Drawing/Visualizing a graph of chain with probabilities
- Higher Order Chains