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1. it would be -
h
^3 / 6 u'''.
2. remember to T-
H
-I-N-K. Remember to think.
3. Instead of saying "That's for him", I say "That's for_(
h
)im".
4. One over
h
squared there,
h
is what?
5. with an error of order
h
^2.
6. probability given that
H
equals 1 is the correct
7. about
H
. We have to choose
H
equals 0 or
H
equals 1.
8.
H
equals 1.
9. For instance, took_(
h
)er, took_(
h
)er.
10. 2200, partial
h
over partial x equals
11. So that's examples of
H
-deletion.
12. divide this by 2h, that'll make it an
h
squared over six.
13. I subtract u(x) - U(x-
h
) and I get two -u(x)'s This is what I
14. And of course, one over
h
squared
15. in most cases, binary random variable,
H
, which is called
16. “We_(
h
)ave a lot to do today”.
17. And this would be, since this step is -
h
now,
18. the consonant or the sound that the word before the word with
H
ends with carries on over
19. I think it comes out wrong by 1/2
h
there.
20. the
h
would have been there.
21. So you could say alright, if
h
is small I can live with that.
22. The number "three", t-
h
-r-e-e is often pronounced
23. So minus u_(i+1) minus 2u_i, minus u_(i-1) over
h
squared
24. distribution... or the proportions are equal if that is what you are looking at.
H
sub
25.
H
-Huh?
26. and something that looks like integral of
h
of x p of x dx.
27. is of order
h
.
28. we’ll probably drop the
H
. On his, on his, monkey on his back. Or, on her, on her, monkey
29. then so once we get here, we're going to finis,
h
and let's turn this project
30. So now that u(x+
h
) - u(x-
h
) is zero,
31. We don't know whether
H
equals 0 or
H
equals 1 is what the
32. The probability of error conditional on
H
equals 0 is
33. Oh and divide by
h
squared.
34. We have to detect whether or not
H
is 1.
35. quantity here is P of Y. So we have probability that
H
equals
36. I want to do T-
H
-I-N--
37. then given
H
equals 0, it's a random walk.
38. But in this model,
H
is either 0 or 1 in the result of this
39. this for
H
equals 0, divide by this for
H
equals 1, and then
40. So some examples of
H
deletion.
41. probability given
H
equals 1 versus error probability given
42. on her back. Check out this video on Dropping the
H
Reductions.
43. BV/KT: (FS) T-
H
-A-N-K-S
44. then what he must be left with is
h
-plus, and since hydrogen
45. means that the limit as x goes to infinity of g over
h
is 1.
46. but they do not come into play until you can say that you have evidence for
H
sub a...
47. So
H
hat is 0 does not mean that
H
itself is 0.
48. KT: (FS) T-
H
-A-N-K-Y-O-U
49.
h
is correct, yeah.
50. This one is also order
h
.
51.
h
is 1/6.
52. we will have
H
sub o set up as beta is equal to zero... The population parameter for slope.
53. So if I bring it up here, bring the
h
squared up here,
54. KT: T-
H
-A
55. u(x+
h
) - u(x), just how much did that step go.
56. itself is neutral, the
h
-plus and the electron had to add up
57. And now, if we condition this on
H
equals 0, then this
58. It's P0 times the probability I make an error given that
H
59. KT: (FS) T-
H
-A-N-K-S
60. Now I want partial
h
over partial x to be zero.
61. But in this case "it_(
h
)as".
62. And it's Sara without an
h
.
63. It's the probability that Y is in A. That means that
H
hat is
64. If you can read this for any
h
, then that's
65. And I may come back and find out why that
h
squared term is.
66. But in this model,
H
is either 0 or 1 in the result of this
67. That first-order approximation, that error of size
h
68. equal to 1 given that
H
is actually 0.
69. (u_1-u_0)/
h
-- that's the approximate slope --
70. now the step is -
h
, but then when I square that step
71. Now, what about this u(x-
h
)?
72. "I took_(
h
)im for_a ride", "You took_(
h
)im for_a ride", etc.
73. This delta x squared is the
h
squared.
74. select
H
equals 0.
75. I'm back to +
h
^2 u''(x) and so on.
76. "That's for_(
h
)im".
77. Hey Sara, without an
h
.
78. so I guess that we really have an
h
^3 / 3 u'''.
79. And then also "We_(
h
)ave a lot to do today".
80. This is the equation T times u divided by
h
squared
81. Instead of saying "It has happened before", you'll hear me say "it_(
h
)as" or just "it's".
82. So that's T divided by
h
squared.
83. "It_(
h
)as happened before".
84. And in fact, the
h
terms, the leading error,
85. Plus
h
^3 upon 6, that's 3*2*1, u triple-prime.
86. You divide by the delta x, the
h
,
87. And of course, in that experiment, that was
H
plus.
88. And given
H
equals 1, it's another random walk.
89. about-- because this is always-- it's either
H
equals
90. How was this delta
H
performing historically?
91. So, the Cockney speaker tends to miss off the "
h
". Okay, so okay that's just a few examples
92. “Did_(
h
)e call you?”
93. It's a random variable under the hypothesis
H
equals 1.
94. which is going to multiply the
h
,
95. A function g of x is tilde, a function
h
of x
96. BV:T-
H
-A-N-K-S.....think...oh! Thank you
97. OK? And so delta
h
over delta y is
98. the ODE. And, y1 of x, notice I don't use a separate letter. I don't use g or
h
or something
99. So instead of "We have a lot to do today", I've said "we_(
h
)ave a lot to do today.
100. How do you figure u(x+
h
)?
101. zero and partial
h
over partial y is less than zero.
102. 0 or
H
equal 1, right?
103. I..."That's a funny one, isn't it? "o-u-g-
h
-t"
104. But otherwise, probability that
H
equals l is the correct
105. We can talk about the probability that
H
is equal to
106. this is correct to order
h
squared.
107. And one more aspect of Cockney is the letter "
h
"...
108. So I always think, T-
H
-I-N-K, T-
H
-I-N-K, remember that.
109. So this thing divided by
h
squared
110. given by half of
h
squared, you see the second order
111. I say “Did_(
h
)e call you?”
112. maximum over l of probability that
H
equals l given Y.
113. KT: how sign T-
H
-A-N-K-S?
114. So this is the probability that
H
is equal to 0 given Y.
115. is-- What's one over
h
squared, just to not forget?
116. So the ratio, the probability that
H
equals 0 given y over
117. It's P0 times the probability I make an error given that
H
118. I've removed the
H
on him and it just sounds like took_(
h
)im took_(
h
)im.
119. asking you to estimate partial
h
over partial y.
120. looks like the expectation of
h
of x for all
h
.
121. And I may come back and find out why that
h
squared term is.
122. and I better remember to divide by
h
squared,
123. The sample values of
H
, you might as well
124.
h
or delta x goes to zero in the end to get the derivative.
125. vector y given
H
equals 0, I'm finding the probability of n
126. 1/2
h
, first order.
127. the probability that
H
is 1 equals y is
128. BV: hey, hey how you sign (fingerspells)T-
H
-A-N-K-S, (FS) T-
H
-A-N-K-S
129. Because this is the probability that
H
is actually
130. Oh and divide by
h
squared.
131. We always set up
H
sub o as no association, no difference, no effect from the treatment,
132. BV/KT: (FS) T-
H
-A-N-K Y-O-U
133. “It_(
h
)as happened before”.
134. We have the
H
-deletion, which is where the
H
at the beginning of words is removed and
135. Now, let's find partial
h
over partial y less than zero.
136. Problem 2B is asking you to find the point at which
h
equals
137. So for example, took_(
h
)im, took_(
h
)im.
138.
H
equals 0.
139. And the answer is the error is of size
h
.
140. “I took_(
h
)er some food.”
141. "Harry" would be pronounced "Arry", and "have" where you make the "
h
" sound "hu", "ave".
142. the value of
h
doesn't change. Which points on the level curve
143. Oh, that's better. All the evil Sarah's spell it with an
h
. Thanks again.
144. Dot-Com no, it's Vince arrow collective Calm. /
h
145. hypothesis
H
equals 0.
146. A function g of x is tilde, a function
h
of x
147. KT: how sign T-
H
-A-N-K Y-O-U
148. we do set up
H
sub o assuming there is no association. That kind of language is going
149. that
H
equals L given Y. We maximize the a posteriori
150. Collective calm /
h
3 if you go to their website right now you get this classy beautiful watch leather wristband
151. error given
H
equals 1 is on the horizontal axis.
152. I heard", so the vowel sound changes: "ear", "er": "hear", "heard", it's spelt "
h
-e-a-r"
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