# Why am I getting wildly incorrect results from FirstPassageTimeDistribution with inexact transition matrix?

Working on some large systems using DiscreteMarkovProcess, I changed the transition matrix to machine precision vs using exact values, which sped things up handily. The only problem was for edge cases, accuracy suffered. I expected that. However, when probing the limits using simple cases, I was surprised to find this happens when I'd not expected it.

A simplified test example:

Table[
intleg = tleg/2;

sa = SparseArray[{{i_, i_} :>
1 - (intleg - i + 1)/tleg, {i_, j_} /;
j == i + 1 :> (1 - i + intleg)/tleg}, {intleg + 1, intleg + 1}];

saN = N[sa, 50];

mp = DiscreteMarkovProcess[1, sa];
mpN = DiscreteMarkovProcess[1, saN];

{tleg, N@Mean[FirstPassageTimeDistribution[mp, intleg + 1]],
N@Mean[FirstPassageTimeDistribution[mpN, intleg + 1]]},

{tleg, {10, 20, 30, 40}}]

(* {{10,22.8333,22.8333},{20,58.5794,58.5793},{30,99.5469,-1.24192},{40,143.91,0.}} *)


This simulates a simple "coupon-collector" situation with a population of tleg distinct items, where only half are "interesting" to me. So a transition matrix is built with appropriate probabilities with an absorbing state when all "interesting" are obtained. That same matrix is converted to inexact via N.

Comparing the results, we see the last two are not even in the zip-code of the correct result. Upping the accuracy/precision of the conversion via N seems to have no effect.

Any ideas what's going on here (I suspect some kind of catastrophic cancellation because of the characteristics of the probabilities)?

• I really don't know. It looks like it could be a bug rather than a numerical problem, as the Precision of the results doesn't decrease very much with respect to the input. But possibly this is just a case that is handled very badly by significance arithmetic. I suspect we need @AndyRoss to tell us what's really going on here? Jun 18, 2014 at 0:16
• This one wasn't my baby so I can't really say for sure what is going on. I suspect that a completely different algorithm is being used for inexact numbers and it is failing. You should file a bug. Jun 18, 2014 at 1:54
• @AndyRoss: I shall do so, however I'll wait a bit to see and responses and if perhaps there's something I missed... Thanks for reply.
– ciao
Jun 18, 2014 at 4:56
• @OleksandrR.: Thanks for reply - yes, it is a strange one, I'm still poking at it, but see AndyRoss' reply...
– ciao
Jun 18, 2014 at 4:56

Thanks for the report, @rasher. This was a bug and it is already fixed in the development version.

In[1]:= Table[intleg = tleg/2;
sa = SparseArray[{{i_, i_} :>
1 - (intleg - i + 1)/tleg, {i_, j_} /;
j == i + 1 :> (1 - i + intleg)/tleg}, {intleg + 1, intleg + 1}];
saN = N[sa, 50];
mp = DiscreteMarkovProcess[1, sa];
mpN = DiscreteMarkovProcess[1, saN];
{tleg, N@Mean[FirstPassageTimeDistribution[mp, intleg + 1]],
N@Mean[FirstPassageTimeDistribution[mpN, intleg + 1]]}, {tleg, {10, 20, 30, 40}}]

Out[1]= {{10, 22.8333, 22.8333}, {20, 58.5794, 58.5794}, {30, 99.5469, 99.5469}, {40, 143.91, 143.91}}


Unfortunately I don't have a workaround at the moment. I apologize for the inconvenience.

• Thanks for confirmation (Are you WRI staff?). Glad to hear it's fixed in upcoming release.
– ciao
Jun 19, 2014 at 21:07
• Yes, I worked on the Markov process functionality. Jun 19, 2014 at 21:33
• Cool! Thanks again for response, I'll go ahead and add bug tag.
– ciao
Jun 19, 2014 at 21:34