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Course Level Date Clip# Line Interlocutor Content
Oral Assessments Graduate 2023-08-01 3 155 S01 oh no I hav- I haven't
Oral Assessments Graduate 2023-08-01 3 156 T01 °mm hmm°
Oral Assessments Graduate 2023-08-01 3 157 S01 see in the left part y and x are all the numbers of
Oral Assessments Graduate 2023-08-01 3 158 S01 points↘ and in the right area (1.2) right part→
Oral Assessments Graduate 2023-08-01 3 159 S01 the four and pi represents the area↘ so (0.6)
Oral Assessments Graduate 2023-08-01 3 160 S01 it's still a knowledge from the class of probability
Oral Assessments Graduate 2023-08-01 3 161 S01 you have previously taken↗ that uh frequency↘
Oral Assessments Graduate 2023-08-01 3 162 T01 mm hmm
Oral Assessments Graduate 2023-08-01 3 163 S01 becomes probability when x and y are (0.6)
Oral Assessments Graduate 2023-08-01 3 164 S01 large numbers↘ (1.2) do you have °any other°
Oral Assessments Graduate 2023-08-01 3 165 T02 so I think I'm- I'm having trouble again what
Oral Assessments Graduate 2023-08-01 3 166 T02 monte carlo is in relationship to what you wrote
Oral Assessments Graduate 2023-08-01 3 167 T02 earlier with random and differ- deterministic↗
Oral Assessments Graduate 2023-08-01 3 168 T02 so can you repeat again the formal definition of this
Oral Assessments Graduate 2023-08-01 3 169 S01 ⌈okay
Oral Assessments Graduate 2023-08-01 3 170 T02 ⌊term
Oral Assessments Graduate 2023-08-01 3 171 S01 monte carlo method is a broad class of computational
Oral Assessments Graduate 2023-08-01 3 172 S01 algorithms that rely on repeated random (1.3)
Oral Assessments Graduate 2023-08-01 3 173 S01 sampling↘
Oral Assessments Graduate 2023-08-01 3 174 T02 °okay°
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