Timeline for Non-Gaussian Hidden Markov Process
Current License: CC BY-SA 4.0
16 events
| when toggle format | what | by | license | comment | |
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| Sep 21 at 17:00 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| May 24 at 16:01 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Jan 26 at 22:56 | comment | added | ydd | My answer here may be helpful for fitting a HMM with arbitrary emission distributions | |
| Jan 25 at 15:08 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Sep 27, 2023 at 15:05 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| May 30, 2023 at 15:05 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Apr 30, 2023 at 13:38 | history | edited | flinty | CC BY-SA 4.0 |
added 3 characters in body
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| Apr 30, 2023 at 13:06 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Mar 31, 2023 at 13:04 | answer | added | unmark1 | timeline score: 0 | |
| Apr 18, 2022 at 23:01 | comment | added | JimB |
I tried SkewNormalDistribution on the TemporalData in the Scope/Estimation section of HiddenMarkovProcess and it took 25 seconds to finish correctly (after two FindRoot warnings).
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| Apr 18, 2022 at 21:52 | comment | added | Daniel Berkowitz | I tried to do SkewedNormalDistrubution[a,b,c] and it did not work. It was taking an extremely long time. They need to work on this feature more. | |
| Apr 18, 2022 at 17:31 | comment | added | JimB |
Thinking about if this question should be closed: In a way the answer "can be found" in the documentation but maybe "easily found" is not correct. Also, using NormalDistribution[a, b], NormalDistribution[103.2, b], and NormalDistribution[a, 17.6] give exactly the same output but NormalDistribution[0, 1] results in an error. So certainly more online documentation would be welcomed. Also, there are no measures of precision given which for me makes the result being of unknown value (except for getting starting values for a function the does give estimates of precision).
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| Apr 18, 2022 at 15:34 | comment | added | JimB |
Yes. Just try EstimatedProcess[data, HiddenMarkovProcess[2, "Gaussian"]] and EstimatedProcess[data, HiddenMarkovProcess[2, NormalDistribution[a, b]]] on the HiddenMarkovProcess/Scope/Estimation example.
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| Apr 18, 2022 at 15:17 | comment | added | Daniel Berkowitz | When I insert the string Guassian I'm really just telling it use a single NormalDistribution[a,b] or is it using a family of Normal Distributions? | |
| Apr 18, 2022 at 4:28 | comment | added | JimB |
One of the examples under Scope/Estimates of HiddenMarkovProcess gives an example for the Exponential distribution. I appears that you can specify the name of any distribution (and that might likely include any "constructed" distribution using TransformedDistribution, etc.).
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| Apr 18, 2022 at 4:07 | history | asked | Daniel Berkowitz | CC BY-SA 4.0 |