Answer:
im peppa pig and this is my little brother George and this is mommy pig and this is daddy pig
Step-by-step explanation:
Complete question
To find 
, Beau found 
and 
. He said that since 5 is between 4 and 9, , is between 2 and 3. Beau thinks a good estimate for , is 
Is his estimate high or low? How do you know?
Answer:
The estimation is high because 5 is very close to 4 so will also be very close to 
which is lower than the estimate
In order to get a good estimate
The first step is to choose a number between 2 and 3 let say 2.8 , 2,85 , 2.9 and the square them
i.e
From here we can see that lies between 2.2 and 2.3 but is closer to 2.2
So a good estimate for is 2.2
Step-by-step explanation:
Answer:
A
Step-by-step explanation:
When X is less than -1, positive parabola.
A negative minus a negative is always a positive ;)
1. Introduction. This paper discusses a special form of positive dependence.
Positive dependence may refer to two random variables that have
a positive covariance, but other definitions of positive dependence have
been proposed as well; see [24] for an overview. Random variables X =
(X1, . . . , Xd) are said to be associated if cov{f(X), g(X)} ≥ 0 for any
two non-decreasing functions f and g for which E|f(X)|, E|g(X)|, and
E|f(X)g(X)| all exist [13]. This notion has important applications in probability
theory and statistical physics; see, for example, [28, 29].
However, association may be difficult to verify in a specific context. The
celebrated FKG theorem, formulated by Fortuin, Kasteleyn, and Ginibre in
[14], introduces an alternative notion and establishes that X are associated if
∗
SF was supported in part by an NSERC Discovery Research Grant, KS by grant
#FA9550-12-1-0392 from the U.S. Air Force Office of Scientific Research (AFOSR) and
the Defense Advanced Research Projects Agency (DARPA), CU by the Austrian Science
Fund (FWF) Y 903-N35, and PZ by the European Union Seventh Framework Programme
PIOF-GA-2011-300975.
MSC 2010 subject classifications: Primary 60E15, 62H99; secondary 15B48
Keywords and phrases: Association, concentration graph, conditional Gaussian distribution,
faithfulness, graphical models, log-linear interactions, Markov property, positive
