I have created a network with Chance nodes whos CPT's are all 1's and 0's.
Is there any computational advantage to using Deterministic nodes in this instance?
Chance vs Deterministic Nodes
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Re: Chance vs Deterministic Nodes
No, there's no explicit check for node type at this point. The logic flow in the inference algorithm is based on the actual values in the CPTs, not on the node type.Is there any computational advantage to using Deterministic nodes in this instance?
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Re: Chance vs Deterministic Nodes
The same answer would then hold for Noisy-Max nodes and CPT.
If I'm creating Noisy-Max nodes they end up being represented as CPTs. There's no advantage to creating them as CPTs in the first place.
(Which means I'd go with Noisy-Max since they're so much easier to set up.)
If I'm creating Noisy-Max nodes they end up being represented as CPTs. There's no advantage to creating them as CPTs in the first place.
(Which means I'd go with Noisy-Max since they're so much easier to set up.)
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Re: Chance vs Deterministic Nodes
They have their noisy weights expanded into full CPTs during inference.If I'm creating Noisy-Max nodes they end up being represented as CPTs
If noisyMax nodes are expressive enough for your purpose, go for it. Additionally, there are parts of SMILE's relevance layer which look for noisyMax nodes and take advantage of their presence to simplify the intermediate data structures created when inference runs.There's no advantage to creating them as CPTs in the first place.