Corrections
This commit is contained in:
parent
62ae348501
commit
bae88b22d7
16
luku24.tex
16
luku24.tex
|
@ -85,7 +85,7 @@ corresponds to the set
|
|||
|
||||
Each outcome $x$ is assigned a probability $p(x)$.
|
||||
Furthermore, the probability $P(A)$ of an event
|
||||
that corresponds to a set $A$ can be calcuted as a sum
|
||||
that corresponds to a set $A$ can be calculated as a sum
|
||||
of probabilities of outcomes using the formula
|
||||
\[P(A) = \sum_{x \in A} p(x).\]
|
||||
For example, when throwing a dice,
|
||||
|
@ -135,7 +135,7 @@ Here $5/6$ is the probability that the outcome
|
|||
of a single throw is not six, and
|
||||
$(5/6)^{10}$ is the probability that none of
|
||||
the ten throws is a six.
|
||||
The complement of this is the answer for the problem.
|
||||
The complement of this is the answer to the problem.
|
||||
|
||||
\subsubsection{Union}
|
||||
|
||||
|
@ -164,7 +164,7 @@ the probability of the event $A \cup B$ is simply
|
|||
|
||||
The \key{conditional probability}
|
||||
\[P(A | B) = \frac{P(A \cap B)}{P(B)}\]
|
||||
is the probability $A$
|
||||
is the probability of $A$
|
||||
assuming that $B$ happens.
|
||||
In this situation, when calculating the
|
||||
probability of $A$, we only consider the outcomes
|
||||
|
@ -172,7 +172,7 @@ that also belong to $B$.
|
|||
|
||||
Using the above sets,
|
||||
\[P(A | B)= 1/3,\]
|
||||
Because the outcomes of $B$ are
|
||||
because the outcomes of $B$ are
|
||||
$\{1,2,3\}$, and one of them is even.
|
||||
This is the probability of an even result
|
||||
if we know that the result is between $1 \ldots 3$.
|
||||
|
@ -318,7 +318,7 @@ The expected value for $X$ in a uniform distribution is
|
|||
\index{binomial distribution}
|
||||
~\\
|
||||
In a \key{binomial distribution}, $n$ attempts
|
||||
are done
|
||||
are made
|
||||
and the probability that a single attempt succeeds
|
||||
is $p$.
|
||||
The random variable $X$ counts the number of
|
||||
|
@ -413,7 +413,7 @@ The next distribution is $[0,1,0,0,0]$,
|
|||
because we can only move from floor 1 to floor 2.
|
||||
After this, we can either move one floor up
|
||||
or one floor down, so the next distribution is
|
||||
$[1/2,0,1/2,0,0]$, etc.
|
||||
$[1/2,0,1/2,0,0]$, and so on.
|
||||
|
||||
An efficient way to simulate the walk in
|
||||
a Markov chain is to use dynamic programming.
|
||||
|
@ -510,7 +510,7 @@ the array in increasing order.
|
|||
It is easy to calculate any order statistic
|
||||
in $O(n \log n)$ time by sorting the array,
|
||||
but is it really needed to sort the entire array
|
||||
to just find one element?
|
||||
just to find one element?
|
||||
|
||||
It turns out that we can find order statistics
|
||||
using a randomized algorithm without sorting the array.
|
||||
|
@ -675,7 +675,7 @@ both colors is $1/2$.
|
|||
In a random coloring, the probability that the endpoints
|
||||
of a single edge have different colors is $1/2$.
|
||||
Hence, the expected number of edges whose endpoints
|
||||
have different colors is $1/2 \cdot m = m/2$.
|
||||
have different colors is $m/2$.
|
||||
Since it is excepted that a random coloring is valid,
|
||||
we will quickly find a valid coloring in practice.
|
||||
|
||||
|
|
Loading…
Reference in New Issue