Foundations of Data Science

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By iterating the above formulas we can arrive at a local optimum for the HMM parameters a i,j and b j (O k ). 9.3 Graphical Models, and Belief Propagation A graphical model is a compact representation of a function of n variables x 1 , x 2 , . . . , x n . It consists of a graph, directed or undirected, whose vertices correspond to variables that take on values from some set. In this chapter, we consider the case where the function is a probability distribution and the set of values the variables take on is finite, although graphical models are often used to represent probability distributions with continuous variables. The edges of the graph represent relationships or constraints between the variables. The directed model represents a joint probability distribution that factors into a product of conditional probabilities. p (x 1 , x 2 , . . . , x n ) = n∏ p (x i |parents of x i ) i=1 It is assumed that the directed graph is acyclic. The directed graphical model is called a Bayesian or belief network and appears frequently in the artificial intelligence and the statistics literature. The undirected graph model, called a Markov random field, can also represent a joint probability distribution of the random variables at its vertices. In many applications the Markov random field represents a function of the variables at the vertices which is to be optimized by choosing values for the variables. A third model called the factor model is akin to the Markov random field, but here the dependency sets have a different structure. In the following sections we describe all these models in more detail. 9.4 Bayesian or Belief Networks A Bayesian network is a directed acyclic graph where vertices correspond to variables and a directed edge from y to x represents a conditional probability p(x|y). If a vertex x has edges into it from y 1 , y 2 , . . . , y k , then the conditional probability is p (x | y 1 , y 2 , . . . , y k ). The variable at a vertex with no in edges has an unconditional probability distribution. If the value of a variable at some vertex is known, then the variable is called evidence. An important property of a Bayesian network is that the joint probability is given by the product over all nodes of the conditional probability of the node conditioned on all its immediate predecessors. In the example of Fig. 9.1, a patient is ill and sees a doctor. The doctor ascertains the symptoms of the patient and the possible causes such as whether the patient was in 308
C 1 S 2 C 2 causes D 1 D 2 diseases S 1 symptoms Figure 9.1: A Bayesian network contact with farm animals, whether he had eaten certain foods, or whether the patient has an hereditary predisposition to any diseases. Using the above Bayesian network where the variables are true or false, the doctor may wish to determine one of two things. What is the marginal probability of a given disease or what is the most likely set of diseases. In determining the most likely set of diseases, we are given a T or F assignment to the causes and symptoms and ask what assignment of T or F to the diseases maximizes the joint probability. This latter problem is called the maximum a posteriori probability (MAP). Given the conditional probabilities and the probabilities p (C 1 ) and p (C 2 ) in Figure 9.1, the joint probability p (C 1 , C 2 , D 1 , . . .) can be computed easily for any combination of values of C 1 , C 2 , D 1 , . . .. However, we might wish to find that value of the variables of highest probability (MAP) or we might want one of the marginal probabilities p (D 1 ) or p (D 2 ). The obvious algorithms for these two problems require evaluating the probability p (C 1 , C 2 , D 1 , . . .) over exponentially many input values or summing the probability p (C 1 , C 2 , D 1 , . . .) over exponentially many values of the variables other than those for which we want the marginal probability. In certain situations, when the joint probability distribution can be expressed as a product of factors, a belief propagation algorithm can solve the maximum a posteriori problem or compute all marginal probabilities quickly. 9.5 Markov Random Fields The Markov random field model arose first in statistical mechanics where it was called the Ising model. It is instructive to start with a description of it. The simplest version of the Ising model consists of n particles arranged in a rectangular √ n × √ n grid. Each particle can have a spin that is denoted ±1. The energy of the whole system depends on interactions between pairs of neighboring particles. Let x i be the spin, ±1, of the i th particle. Denote by i ∼ j the relation that i and j are adjacent in the grid. In the Ising 309
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Foundations of Data Science ∗ Avr
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4.4 Branching Processes . . . . . .
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8.2.2 Structural properties of the
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12.4.7 Median . . . . . . . . . . .
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densities, discrete optimization, e
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2 High-Dimensional Space 2.1 Introd
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Also, if x and y are independent, t
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1 1 − ɛ Annulus of width 1 d Fig
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integral gives ∫ ∞ 0 e −r2 r
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The volume of the hemisphere below
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points onto the circle. In higher d
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Using the substitution 2z = y 2 , |
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For the nearest neighbor problem, i
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√ √ 2d+O(1) ≤ 2d + ∆2 −O(
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2.10 Bibliographic Notes The word v
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Exercise 2.9 A 3-dimensional cube h
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Exercise 2.26 Explain how the volum
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Exercise 2.40 Consider a non orthog
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1 Exercise 2.50 Use the probability
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This decomposition of A can be view
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3.3 Singular Vectors We now define
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orthonormal basis w 1 , w 2 , . . .
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A n × d = U n × r D r × r V T r
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3.6 Left Singular Vectors Theorem 3
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Lemma 3.10 (Analog of eigenvalues a
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Proof: Let A = r∑ σ i u i vi T i
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a i , let dist(a i , l) denote its
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such models. Mixture models are a v
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1. The best fit 1-dimension subspac
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This leads to the following theorem
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Thus, the maximum cut problem can b
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and this meets the claimed error bo
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(0,3) (1,1) (3,0) ⎛ M = ⎝ 1 1 0
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Exercise 3.18 Modify the power meth
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2. What percent of the Forbenius no
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City Bei- Tian- Shang- Chong- Hoh-
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5 10 15 20 25 30 35 40 4 9 14 19 24
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Prob(vertex has degree k) = ( ) n
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and thus k k ≤ n. Since k! ≤ k
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For x to be equal to zero, it must
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No items E(x) ≥ 0.1 At least one
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Our first task is to figure out wha
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√ Thus, the probability for all s
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Figure 4.7: A degree three vertex w
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ftp://ftp.cs.rochester.edu/pub/u/jo
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Size of frontier 0 ln n nθ n Numbe
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For a small number i of steps, the
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same or are disjoint, ⎛( n∑ )
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are O(ln n) in size and the expecte
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f(x) q p 0 f(f(x)) f(x) x Figure 4.
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may be infinite. That is, ∞∑ i=
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Property cycles 1/n giant component
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Keep in mind that the leading terms
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4.6 Phase Transitions for Increasin
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p(n) A symmetric argument shows tha
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simple. We supply some intuition be
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Proof: Say that t is a “busy time
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Consider a graph in which half of t
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problem contributes a minuscule err
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one. On the other hand, for δ = 1,
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for all δ greater than δ critical
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expected degree d i = 1, 2, 3 √ t
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Destination Figure 4.17: For d < 2,
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For at least half of the pairs of v
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S i+1 is (1 − 1 ) |Si| ≤ 1 −
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Exercise 4.7 Let f (n) be a functio
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Exercise 4.19 (Birthday problem) Wh
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Exercise 4.38 Consider a model of a
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Exercise 4.53 Consider graph 3-colo
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5 Random Walks and Markov Chains A
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A B C (a) A B C (b) Figure 5.1: (a)
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in time polynomial in n. A quantity
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5.2 Markov Chain Monte Carlo The Ma
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p(a) = 1 2 p(b) = 1 4 p(c) = 1 8 p(
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5 8 7 12 1 3 1 6 1 8 1 3 3,1 3,2 3,
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Figure 5.4: A network with a constr
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There is a simple interpretation of
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Let γ i = π 1 + π 2 + · · · +
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5.4.1 Using Normalized Conductance
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The Gaussian distribution on the in
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6 6 1 8 1 5 8 4 5 5 Graph with boun
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and then reaching a from y before r
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every vertex? Hitting time The hitt
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clique of size n/2 x y } {{ } n/2 F
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i ↑ ↓ ↑ ↓ ↑ j ↑ =⇒ i
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Theorem 5.13 Let G be an undirected
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0 1 2 3 4 12 20 Number of resistors
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1 2 4 Figure 5.11: Paths obtained f
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whereas, with k self-loops, the equ
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or p[I − (1 − α)A] = α (1, 1,
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Exercise 5.10 Let p be a probabilit
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i 1 i 2 R 1 R 2 R 3 Figure 5.13: An
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(a) 1 2 3 4 (b) 1 2 3 4 (c) 1 2 3 4
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Exercise 5.40 Suppose that the cliq
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Exercise 5.55 Using a web browser b
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will allow us to make mathematical
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Not spam { }} { { Spam }} { x 1 x 2
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1 ∨ x 8 = 1}, or more succinctly,
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described using O(k log d) bits: lo
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In fact, we can show this bound is
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y definition of w ∗ . Similarly,
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y x φ Figure 6.4: Data that is not
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Theorem 6.11 (Online to Batch via R
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function of concept class H, will g
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Corollary 6.16 (VC-dimension sample
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A sphere in d-dimensions is a set o
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then with probability ≥ 1 − δ,
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work on a variety of measures. One
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Since weight(0) = n, the total weig
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Stochastic Gradient Descent: Given:
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sleeping experts problem. Combining
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⎫ ⎪⎬ ⎪⎭ Each gate is conn
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W 1 W 2 W 1 W 2 W 3 (a) (b) Figure
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convolution pooling Image Convoluti
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discard separators in advance that
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6.14.2 Active learning Active learn
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6.16 Exercises Exercise 6.1 (Sectio
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√ L log(L(T )) |S|) will be at mo
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7 Algorithms for Massive Data Probl
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important to know if some popular s
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We now give an example of a 2-unive
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estimate of m. To obtain a coin tha
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expected value of the sum will be z
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δ have values in ±1. There are ex
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mean. (ii) There is “no free lunc
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⎡ ⎢ ⎣ A m × n ⎤ ⎡ ⎥
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One uses the primitive in Section ?
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would require s ≥ n, which clearl
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Page 263 and 264: Exercise 7.30 Blast: Given a long s
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Page 267 and 268: B A Figure 8.1: Example where the n
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Page 333 and 334: In the discrete case, x = [x 0 , x
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11.3 Wavelet Systems So far we have
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11.5 Conditions on the Dilation Equ
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the support of both sides of the eq
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and the wavelet functions are ortho
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11.7 Sufficient Conditions for the
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Example of orthogonality when wavel
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11.9 Designing a Wavelet System In
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3. f(x) = 1 2 f(2x) + 1 2 f(2x −
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12 Appendix 12.1 Asymptotic Notatio
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these equalities by derivatives.
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Gaussian and related integrals To v
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1 + x ≤ e x for all real x (1 −
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Thus, n! ≤ n n e −n√ ne. For
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from which the current inequality f
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Example: Let f (x) = x k for k an e
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Random variables x 1 , x 2 , . . .
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12.4.7 Median One often calculates
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The density of y is the unit varian
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Generation of random numbers accord
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Example: Consider flipping a coin 1
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Setting λ = ln(1 + δ) Prob ( s >
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12.5 Bounds on Tail Probability Aft
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Collect terms of the summation with
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The first integral is just the stan
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of a symmetric matrix, counting mul
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capture this situation. Now AA T =
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The above theorem tells us that the
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Important special cases are |x| 0 t
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Lemma 12.23 Let A be a symmetric ma
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etween vertices in different blocks
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∞∑ ∑ ix i = ∞ i=0 i=0 x d d
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Generating functions are useful for
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Thus, u n = (n − 1) u n−2 . The
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f(x) a c b Figure 12.3: Illustratio
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need to argue that no other sequenc
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1. How large must δ be if we wish
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Exercise 12.40 We are given the pro
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Index 2-universal, 240 4-way indepe
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Lagrange, 423 Law of large numbers,
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References [AK] Sanjeev Arora and R
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[MR95b] [MU05] [MV10] Rajeev Motwan
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