Run longer to get better answers?

[Tom Lieber]

I'm not familiar enough with the inference algorithms to choose good parameters yet, but I do know how long I'm willing to wait for an answer. Is there a way to run for a particular amount of time instead of for a particular number of steps?

Related to that, is there a way to run inference such that it picks up where it left off? For example, run MCMC for 100 steps, print the result of a query, run for 100 steps more, print improved results, etc.

[null-a]

Hi. It's been a while since I worked with WebPPL, but fwiw, as best I remember...

> Is there a way to run for a particular amount of time instead of for a particular number of steps?

In general, I'm afraid not. (Though I think inference by enumeration might have this as an undocumented feature.)

> Related to that, is there a way to run inference such that it picks up where it left off?

Again, I don't think so.

Best,

Paul

[Hakan Kjellerstrand]

On Wed, Jan 17, 2024 at 4:19 PM null-a <hors...@gmail.com> wrote:

Hi. It's been a while since I worked with WebPPL, but fwiw, as best I remember...

> Is there a way to run for a particular amount of time instead of for a particular number of steps?

In general, I'm afraid not. (Though I think inference by enumeration might have this as an undocumented feature.)

The feature is maxRuntimeInMS (where MS=milliseconds), and is only supported for method="enumerate".

Example:

    var d = Infer({method:"enumerate",maxRuntimeInMS:6000000},model)

Best,

Hakan

On Sunday, January 14, 2024 at 2:52:50 PM UTC all...@gmail.com wrote:

I'm not familiar enough with the inference algorithms to choose good parameters yet, but I do know how long I'm willing to wait for an answer. Is there a way to run for a particular amount of time instead of for a particular number of steps?

Related to that, is there a way to run inference such that it picks up where it left off? For example, run MCMC for 100 steps, print the result of a query, run for 100 steps more, print improved results, etc.

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Hakan Kjellerstrand
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[Tom Lieber]

Thanks, all.