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