Comments (6)
Sorry - I wrote it pretty vaguely!
So if I understand @ericagol's proposal for the missing data algorithm, we just need to remove all the rows of a
, U
, and V
that correspond to the missing data (P
is actually trickier in that form - more on that later!). So if you only observe one band at each time, this would be equivalent to just multiplying each row of U
and V
by the alpha
value for the band observed at that time and multiplying that row of a
by the square (modulo the diag
entries, but you know what I mean!). So in its simplest form, Alpha
would be an N
-vector where each entry is the alpha
parameter for the band at time n
. The reason why I said NxJ
is that if you add KronTerms that is equivalent to allowing different alpha
s for different J
s and we might want to support that.
Is this at all clearer? Hard to explain without a whiteboard perhaps when it's all a little muddled in my brain.
Also: the reason why the P
matrix is harder when masking is because most of the rows of P
are 1, I think, but we need the row corresponding to the first observed band of P
to be evaluated as exp(-c (t_n - t_{n-1}))
(currently it's the first band, which will change if that band gets masked). This is trivial in the "one band per time" case as described above.
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Sure that would be great! Next week is getting a bit overloaded already. Perhaps some time on Nov 3? Y'all vote by mail in Washington right?
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@dfm I'm trying to understand your suggestion for the low-rank version. What determines the entries in Alpha
?
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@dfm Perhaps we should meet about this; I haven't thought about this much, and so could use an explainer.
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@dfm Voted!
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Election day works for me!
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