From ca8275cb9261f52149c758e4c5b1742cb697e4ba Mon Sep 17 00:00:00 2001 From: Margo van der Stam Date: Tue, 3 Feb 2015 23:20:56 +0100 Subject: [PATCH] reflections --- report/ass2-1.tex | 2 ++ report/ass2-2.tex | 5 ++++- 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/report/ass2-1.tex b/report/ass2-1.tex index 6a07cda..1bbceac 100644 --- a/report/ass2-1.tex +++ b/report/ass2-1.tex @@ -335,4 +335,6 @@ A Bayesian network representation of the extended story is possible, but could \hline \end{tabular} +\section{Additional Questions} +We wouldn't change any aspect of the assignment. It is nice that the assignment is slowly increasing in difficulty because of the extention of the story. We estimate that we spent about 20 hours each on the assignment. diff --git a/report/ass2-2.tex b/report/ass2-2.tex index 40d1ab3..ebf9c69 100644 --- a/report/ass2-2.tex +++ b/report/ass2-2.tex @@ -129,8 +129,11 @@ Answer: P(whatpizza|Obs)=[0.28994000000000003,0.33126500000000003]. The model is relatively effective. One can easily compute the most likely pizza configuration from abstract shapes only. The model is also very scalable since the model only grows with the number of ingredients per pizza added because all -the pizzas are independent. Because of this large datasets can be added very +the pizzas are independent. Because of this, large datasets can be added very easily and because of this the computational complexity does not increase very much. Changes in parameters can also be added very easily, for example when a more accurate low level detection technique is applied the probabilities for the shapes present can be increased very easily. + +\section{Additional Questions} +If we could change a thing about the second assignment, we would try to make it slightly less abstract what the meaning of the assignment was. We estimate that we spent about 14 hours each on the assignment. -- 2.20.1