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1G (Linear Algebra)

Words: 39 Articles: 4

a

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Solution

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Bilinearity and symmetry follow from the integral. Also , and equality forces the continuous polynomial to vanish identically.
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b

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Solution

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Gram–Schmidt applied to gives the orthonormal basis and .
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2F (Topological Spaces)

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Solution

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A neighbourhood of contains an open set containing ; when every neighbourhood eventually contains every . If two metrics have the same topology they plainly have the same convergent sequences. Conversely, if a -open set were not -open, some would have points with ; the common-sequence assumption would imply , contradicting -openness. Symmetry finishes the proof. This fails generally: on an uncountable set the discrete and cocountable topologies differ, but in both every convergent sequence is eventually constant.
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3.1G

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a

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Solution
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The residue is the coefficient in the Laurent expansion. At a double pole, is holomorphic and , so .
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b

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Solution
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Partial fractions give
For this becomes
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3.2C

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Solution

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At zero: is holomorphic with a zero; has a simple pole of residue ; has a removable singularity with value ; is essential with residue ; is essential with residue . Finally has poles accumulating at zero, so zero is a non-isolated singularity and no residue there is defined.
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4B (Variational Principles)

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Solution

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Fermat’s principle makes stationary. Writing the ray as gives a Lagrangian independent of , and its first integral is . At the origin . At , , so and the forward angle is .
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5C (Numerical Analysis)

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i

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Solution

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Successive substitution shows every stage is a polynomial in times , of degree at most its stage number. Hence with a degree- polynomial.
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ii

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Solution

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Order requires on the test equation, fixing all coefficients: .
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iii

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Solution

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For , , so only is stable. For , , so the stable imaginary segment is .
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6H (Statistics)

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a

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Solution

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Factorisation of the likelihood shows that is sufficient: conditional on it, the sample law is independent of .
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b

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Solution

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The Rao–Blackwell theorem says that for convex loss, conditioning any estimator on a sufficient statistic cannot increase risk; for squared error it preserves the mean and weakly reduces variance.
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c

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Solution

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Since , take . It is unbiased, and the supplied variance formula gives , which is its MSE.
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7H (Optimisation)

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Solution

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Replace the quadratic constraint by the two linear inequalities . Simplex pivots lead to the active constraints , , and . Thus and the value is . With right-hand side , the same basis remains optimal for small nonzero , giving .
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8E (Linear Algebra)

Words: 135 Articles: 12

a

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Solution

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A Jordan form is block diagonal with blocks having on the diagonal and ones on the superdiagonal. Over every square matrix is similar to one, uniquely up to permutation of blocks.
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b

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i

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Solution
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lowers total degree, so it is nilpotent and has only eigenvalue zero. Solving gives ; the degree restrictions give the eigenspace .
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ii

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Solution
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Direct degree counting gives for . Hence the Jordan block sizes are .
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c

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i

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Solution
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On each Jordan block of , is triangular with diagonal entry ; hence the eigenvalues, with multiplicity, are .
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ii

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Solution
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The shift matrix obeys and . Evaluating at gives
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9E (Groups, Rings and Modules)

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i

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Solution

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If , then every term in vanishes; also . Thus the nilpotents form an ideal . The quotient has no nonzero nilpotents.
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ii

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Solution

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One direction follows because finitely many nilpotent coefficients generate a nilpotent ideal. Conversely, reducing a nilpotent polynomial modulo gives a nilpotent polynomial over the reduced ring ; comparing its highest nonzero coefficient shows it must be zero, so every coefficient lay in .
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iii

Words: 30 Articles: 1

Solution

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If , then . For the polynomial, its positive-degree part is nilpotent by (ii), so adding it to the unit gives a unit.
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iv

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Solution

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Choose nonzero of least degree with . If is its leading coefficient, then the top coefficient equation gives ; minimality applied after removing the top term forces . In , a nonzero annihilator therefore satisfies for every ; a prime divisor of divides all and , so the displayed gcd exceeds one.
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10G (Analysis II)

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a

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Solution

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Uniform convergence means: for every there is such that implies for every .
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b

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Solution

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If and , then every tail is bounded uniformly by the corresponding numerical tail. The uniform Cauchy criterion therefore proves uniform convergence.
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c

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Solution

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For each , on and the p-series converges. The M-test gives locally uniform convergence there, so the sum is continuous on .
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d

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i

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Solution
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The series for converges uniformly by the M-test. Therefore termwise integration is valid and every term integrates to , so the integral is zero.
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ii

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Solution
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It is not uniform. For a small fixed , set . On a block , all lie in a compact subinterval of , so the corresponding tail has a positive lower bound independent of ; this violates the uniform Cauchy criterion.
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11.1G

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a

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Solution
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Apply the maximum modulus principle to , with its removable value , to obtain . Equality at a nonzero interior point makes constant, so with .
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b

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Solution
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Let and . The MΓΆbius map sends the two circular arcs through to rays bounding a wedge of angle . After choosing the branch and a unimodular constant so the interior has positive argument, maps that wedge bijectively onto the upper half-plane.
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11.2C

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i

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Solution
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Off the slit choose the stated analytic square-root branch. Differentiation gives , which does not vanish there, so the map is conformal.
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ii

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Solution
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Multiplying gives the reciprocal identity and hence .
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iii

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Solution
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For ,
and . Thus , . Nested level curves and the behaviour at infinity show that exteriors correspond.
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iv

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Solution
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The MΓΆbius map sends to the imaginary axis and to ; testing one exterior point shows that maps to .
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v

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Solution
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The required harmonic function is
On the upper and lower boundary its argument is respectively and , while at infinity the logarithm tends to .
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12D (Methods)

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i

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Solution

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For the odd two-periodic extensions, with , while has . Thus the first has convergence because its periodic extension jumps, while the smoother second extension has coefficients.
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ii

Words: 39 Articles: 1

Solution

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Writing gives , hence and . The Sturm–Liouville form is , so for . Expanding the source gives coefficients , hence
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13B (Quantum Mechanics)

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i

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Solution

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The nonrelativistic SchrΓΆdinger Hamiltonian uses and therefore has no massless limit; massless particles require a relativistic wave equation or quantum field theory.
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ii

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Solution

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The walls give ; normalisation gives , and . Product-to-sum integration over gives .
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iii

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Solution

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Using the quadratic form of the Hamiltonian,
.
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iv

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Solution

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Only odd stationary states contribute, and for their squared coefficients are . Since , substitution gives
so .
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14D (Electromagnetism)

Words: 88 Articles: 4

a

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Solution

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Using , the divergence is a line integral of a total derivative, zero for a closed wire or suitable vanishing endpoints. Taking the curl and moving it under the integral gives the stated Biot–Savart formula.
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b

Words: 50 Articles: 1

Solution

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The negative- segment integrates to the stated azimuthal field. The negative- segment contributes
Adding this to the first field is the complete answer. On , the field tends to as and to as .
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15A (Fluid Dynamics)

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a

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Solution

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Taking the curl of Euler and using incompressibility gives , equivalently .
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b

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i

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Solution
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The divergence is .
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ii

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Solution
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For uniform vertical vorticity, advection vanishes and , hence .
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iii

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Solution
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. Positive stretches material lines vertically; conservation of vortex flux through the shrinking transverse area amplifies vorticity.
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16C (Numerical Analysis)

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i

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Solution

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For fixed add to so the resulting function also vanishes at . Repeated Rolle gives a point where its st derivative vanishes, yielding the formula.
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ii

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Solution

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The recurrence shows that has leading coefficient . Since its roots are the interpolation nodes, , and on .
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iii

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Solution

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Since , the error is at most uniformly.
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iv

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Solution

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At , direct substitution gives a product of odd half-integers. Taking logarithms and applying the hinted lower Riemann-sum estimate yields , hence .
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17H (Statistics)

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i

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Solution

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With , and .
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ii

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Solution

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The statistic is , with an null distribution.
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iii

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Solution

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Under the null, , so for .
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iv

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Solution

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The displayed linear form is centred normal with variance . The normal tail bound proves the claim.
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v

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Solution

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By (iv) and the union bound, . Setting bounds the size by .
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vi

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Solution

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Scale cancels in
Because under the null, it has exactly the requested distribution; its quantiles can be simulated without knowing .
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18H (Markov Chains)

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a

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Solution

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A stopping time has determined by . The Strong Markov property says that, conditional on and , the post- chain is a fresh chain started at , independent of the pre- history.
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b

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i

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Solution
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First-step equations and give .
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ii

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Solution
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Augment by whether the last sound was a growl. The equations , , give .
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c

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Solution

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Augment the state by the current sound and , and solve the translation-invariant first-step equations. Equivalently, solve the Poisson equation for the additive reward . The stationary probabilities are , so the drift is per sound. The correction term has the same value at the initial and terminal bark states; optional stopping at therefore gives the expected produced length .
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