Timeline for Stationary distribution in general Markov Chains
Current License: CC BY-SA 3.0
10 events
| when toggle format | what | by | license | comment | |
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| Jan 6, 2014 at 21:58 | vote | accept | Denis | ||
| Jan 3, 2014 at 20:21 | answer | added | guest | timeline score: 3 | |
| Jan 3, 2014 at 19:55 | comment | added | Denis | I also clarified what I mean by "stationary distribution" | |
| Jan 3, 2014 at 19:45 | history | edited | Denis | CC BY-SA 3.0 |
added 243 characters in body
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| Jan 3, 2014 at 19:40 | comment | added | Denis | You're right that initial state is important here, I will add it in the question. | |
| Jan 3, 2014 at 19:25 | history | edited | Ricardo Andrade | CC BY-SA 3.0 |
added top level tag; corrected typos
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| Jan 3, 2014 at 18:50 | comment | added | guest | @piyush Markov processes that do not have stationary distributions can still have limiting distributions in certain senses. I think the OP's question is about these distributions that might not technically be stationary distributions by definition. | |
| Jan 3, 2014 at 18:33 | comment | added | Piyush Grover | Stationary distribution by definition is independent of initial state. I would think the corresponding weights of the "good" stationary dist. should be (0,5/16,11/32,11/32). | |
| Jan 3, 2014 at 18:19 | comment | added | guest | In the general case, this would depend on your initial state. Although it doesn't address the full generality of your question, the Wikipedia page about absorbing Markov chains is pretty good en.wikipedia.org/wiki/Absorbing_Markov_chain | |
| Jan 3, 2014 at 18:05 | history | asked | Denis | CC BY-SA 3.0 |