00:00:00
OPERATOR: The following
content is provided under a
00:00:01
Creative Commons license.
00:00:03
Your support will help MIT
OpenCourseWare continue to
00:00:06
offer high quality educational
resources for free.
00:00:10
To make a donation, or view
additional materials from
00:00:13
hundreds of MIT courses,
visit MIT OpenCourseWare
00:00:19
PROFESSOR: So you may recall
that, in the last lecture, we
00:00:25
more or less solved the drunken
student problem, looking
00:00:29
at a random walk.
00:00:32
I now want to move on and
discuss some variants of the
00:00:36
random walk problem that are
collectively known as
00:00:41
biased random walks.
00:00:53
So the notion here is, the walk
is still stochastic but there
00:00:58
is some bias in the direction,
so the movements are not
00:01:02
uniformly distributed or
equally distributed
00:01:05
in all directions.
00:01:07
As I go through this, I want
you to sort of, think in
00:01:11
advance about what the
take-home message should be.
00:01:16
One, and this is probably the
most important part for today,
00:01:20
is I want to illustrate how by
designing our programs in a
00:01:24
nice way around classes, we can
change them to do something
00:01:30
else writing a minimal
amount of code.
00:01:35
And the idea here is that
classes are not there just to
00:01:39
provide some syntax that makes
life complicated, but to
00:01:42
actually provide a mechanism
that lets us structure our
00:01:46
programs in a way that we
can modify them later.
00:01:52
I also want to give you a bit
more experience looking at
00:01:55
data, and understanding how,
when you get the results of a
00:01:59
simulation or anything else
that provides you with data,
00:02:04
you can plot, them study the
plots, and try and use those
00:02:08
to develop an understanding
about what's going on.
00:02:12
And finally, I want to just
get you thinking more
00:02:16
about random behavior.
00:02:18
Because we're going to talk a
lot more this semester and
00:02:20
in general throughout your
careers, you'll see a lot of
00:02:24
uses of stochastic things.
00:02:26
All right, so let's think
about these biased walks.
00:02:31
So for example, assume that the
drunk grew up in South Florida,
00:02:37
and really hates the
New England winter.
00:02:40
Well there might be a bias that
even though the drunk is not in
00:02:44
total control, she's kind
of wandering southward.
00:02:48
So there's a bias when taking
a step to more likely go
00:02:51
south than certainly north.
00:02:55
Or imagine that the drunk is
photosensitive and moves either
00:02:59
towards the sun or away from
the sun, or some such thing.
00:03:04
So we're going to look at
different things of that nature
00:03:07
and think about, I'll come back
to Chutes and Ladders later in
00:03:10
the lecture, you can speculate
on why this is here.
00:03:19
And we'll look at that.
показать еще
русский▼
English
español
italiano
日本語
中文
português
français
Deutsch
