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```Chernoff Bounds, and etc.
Presented by Kwak, Nam-ju
Topics
A General Form of Chernoff Bounds
 Brief Idea of Proof for General Form of
Chernoff Bounds
 More Tight form of Chernoff Bounds
 Application of Chernoff Bounds:
Amplification Lemma of Randomized
Algorithm Studies
 Chebyshev’s Inequality
 Application of Chebyshev’s Inequality
 Other Considerations

A General Form of Chernoff Bounds
Assumption
 Xi’s: random variables where Xi∈{0, 1} and
1≤i≤n.
 P(Xi=1)=pi and therefore E[Xi]=pi.
 X: a sum of n independent random
variables, that is,

 μ: the
mean of X
A General Form of Chernoff Bounds

When δ >0
Brief Idea of Proof for General Form of Chernoff Bounds
Necessary Backgrounds
 Marcov’s Inequality
For any random variable X≥0,

 When
f is a non-decreasing function,
Brief Idea of Proof for General Form of Chernoff Bounds
Necessary Backgrounds
 Upper Bound of M.G.F.

Brief Idea of Proof for General Form of Chernoff Bounds

Proof of One General Case
 (proof)
Brief Idea of Proof for General Form of Chernoff Bounds

Proof of One General Case
Here, put a value of t which minimize the
above expression as follows:
Brief Idea of Proof for General Form of Chernoff Bounds

Proof of One General Case
As a result,
More Tight form of Chernoff Bounds
The form just introduced has no
limitation in choosing the value of δ other
than that it should be positive.
 When we restrict the range of the value
δ can have, we can have tight versions of
Chernoff Bounds.

More Tight form of Chernoff Bounds

When 0<δ<1

Compare these results with the upper bo
und of the general case:
Application of Chernoff Bounds:
Amplification Lemma of Randomized Algorithm Studies



A probabilistic Turing machine is a
nondeterministic Turing machine in which
each nondeterministic step has two choices.
(coin-flip step)
Error probability: The probability that a
certain probabilistic TM produces a wrong
Class BPP: a set of languages which can be
recognized by polynomial time probabilistic
Turing Machines with an error probability of
1/3.
Application of Chernoff Bounds:
Amplification Lemma of Randomized Algorithm Studies
However, even though the error
probability is over 1/3, if it is between 0
and 1/2 (exclusively), it belongs to BPP.
 By the amplification lemma, we can
construct an alternative probabilistic
Turing machine recognizing the same
language with an error probability 2-a
where a is any desired value. By adjusting
the value of a, the error probability would
be less than or equal to 1/3.

Application of Chernoff Bounds:
Amplification Lemma of Randomized Algorithm Studies
How to construct the alternative TM?
(For a given input x)
1. Select the value of k.
2. Simulate the original TM 2k times.
3. If more than k simulations result in
accept, accept; otherwise, reject.
 Now, prove how it works.

Application of Chernoff Bounds:
Amplification Lemma of Randomized Algorithm Studies
Xi’s: 1 if the i-th simulation produces a
 X: the summation of 2k Xi’s, which means
simulations among 2k ones.
 ε: the error probability
 X~B(2k, ε)
 μ=E[X]=2k ε

Application of Chernoff Bounds:
Amplification Lemma of Randomized Algorithm Studies
P(X>k): the probability that more than hal
f of the 2k simulations get a wrong answe
r.
 We will show that P(X>k) can be less tha
n 2-a for any a, by choosing k appropriatel
y.

Application of Chernoff Bounds:
Amplification Lemma of Randomized Algorithm Studies
Here we set δ as follows:
Therefore, by the Chernoff Bounds,
Application of Chernoff Bounds:
Amplification Lemma of Randomized Algorithm Studies
To make the upper bound less than or eq
ual to 2-a,
Application of Chernoff Bounds:
Amplification Lemma of Randomized Algorithm Studies
Here, we can guarantee the right term is po
sitive when 0<ε<1/2.
Chebyshev’s Inequality

For a random variable X of any
probabilistic distribution with mean μ and
standard deviation σ,

To derive the inequality, utilize Marcov’s in
equality.
Application of Chebyshev’s Inequality
Use of the Chebyshev Inequality To
Calculate 95% Upper Confidence Limits
for DDT Contaminated Soil
Concentrations at a
 Using Chebyshev’s Inequality to
Determine Sample Size in Biometric
Evaluation of Fingerprint Data Superfund
Site.

Application of Chebyshev’s Inequality

For illustration, assume we have a large
body of text, for example articles from a
publication. Assume we know that the
articles are on average 1000 characters
long with a standard deviation of 200
characters. From Chebyshev's inequality
we can then deduce that at least 75% of
the articles have a length between 600
and 1400 characters (k = 2).
Other Considerations
The only restriction Markov’s Inequality
impose is that X should be non-negative.
It even doesn’t matter whether the
standard deviation is infinite or not.
 e.g. a random variable X with P.D.F.

it has a finite mean but a infinite standard
deviation.
Other Considerations

P.D.F.

E[X]

Var(x)
Conclusion
Chernoff’s Bounds provide relatively nice
upper bounds without too much
restrictions.
 With known mean and standard deviation,
Chebyshev’s Inequality gives tight upper
bounds for the probability that a certain
random variable is within a fixed distance
from the mean of it.

Conclusion

Any question?
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