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A Selection of Problems from A.A. Markov’s Calculus of Probabilities: Problem 3 – Solution to Parts 3, 4 and 5

Author(s): 
Alan Levine (Franklin and Marshall College)

 

Review statement of Problem 3.

 

The other probabilities referred to in the problem represent three particular cases of the probability just found and, therefore, can be gotten from the expression ΔlZnilZn for particular assumptions about i and l: 1) i=0,    2) i=nl,    3) i=0,l=n.

Letting i=0, we get the following expression for the probability of the appearance of l specific numbers: ΔlZnlZn=1l1(nmn)k+l(l1)12(nmn)k(nm1n1)k

Letting i=nl, we find that the probability of the appearance of l specific numbers and the nonappearance of the remaining is equal to:

ΔlZ0Zn=ZlZnl1Zl1Zn+l(l1)12Zl2Zn=(nmn)k(nm1n1)k(lm+1l+1)k{1l1(lml)k+}.
 Finally, the probability of the appearance of all n numbers is equal to:
ΔnZ0Zn=1n1(nmn)k+n(n1)12(nmn)k(nm1n1)k.

Stopping at the last formula and noting that it requires tedious calculation for large values of n, we derive two approximate formulas for it.

For getting the first approximate formula, assume that all numbers (nm1n1)k, (nm2n2)k,, are equal to (nmn)k which, for brevity, we denote by the letter t.

Under this assumption, the indicated formula immediately gives us: ΔnZ0Zn(1t)n, where t=(nmn)k.

For the second approximation, we note that for small values of i, the ratio (nmini)k:(nmn)k, which is equal to {1im(ni)(nm)}k, differs little from 1kimn(nm), and the product (1kmn(nm))(12kmn(nm))(1ikmn(nm)) differs little from 1km(1+2++i)n(nm)=1kmi(i+1)2n(nm).

On this basis, we accept ti+1(1kmi(i+1)2n(nm)) as the approximate value of each product (nmn)k(nm1n1)k(nmini)k.

Substituting this approximate expression in place of the exact one in the formula, we get ΔnZ0Zn(1t)nkmt2(n1)2(nm)(1t)n2(1t)n{1kmt22}, since we assume the numbers n1nm and 1(1t)2 are close to 1.

We apply our approximate formula to finding the number of draws under the hypothesis that the probability of the appearance of all numbers was approximately equal to a given number 1C.

The first approximation formula gives (1t)n1C from which we deduce nlog(1t)ntlogC; but t=(nmn)k and, thus, logt=klog(1mn)kmn.11

Comparing the approximate equations ntlogC and logtkmn, we find kn(lognloglogC)m.

In the subsequent calculation, let logCn=t0 and n(lognloglogC)m=k0, so that t0 and k0 are the approximate values of the numbers t and k.

The second approximate expression of the probability gives (1t)n(1kmt22)1C, from which, by performing the approximate calculations, we deduce
logCnt+nt22+kmt22nt+(n+k0m)t202,
tlogCn[1(n+k0m)t202logC],
and then logtlognloglogC+(n+k0m)t202logC.

On the other hand, we have logt=klog(1km)kmn+k0m22n2.

Finally, equating one approximate expression for $\log t$ to the other, we arrive at the approximate equation kmn+k0m22n2lognloglogC+(n+k0m)t202logC, from which we easily deduce
knm{lognloglogC+k0m2n2(logCm)+12nlogC}1m{(lognloglogC)(n+12logCm2)+12logC}.

For example, let us assume n=90, m=5, C=2.
 
Then

logn=4.4998, logC=0.69314,
loglogC=0.3665, n+12logCm2=87.84657

and, performing a simple calculation by the previous approximate formula, we get k=4.8663×87.8466+0.346585.5.

According to this result, it can be verified that the probability of the appearance of all 90 numbers in 85 draws is somewhat less than one-half, and for 86 draws is already bigger than one-half.

 

Continue to statement of Problem 4.

 


[11] Here, Markov used “log” to denote natural (base e) logarithms. Later, he used the same notation for common (base 10) logarithms!

The Taylor series for ln(1t) is tt22t33t, for small values of t. (The series converges for 1t<1.)  Later, he included the quadratic term for a better approximation.

 

Alan Levine (Franklin and Marshall College), "A Selection of Problems from A.A. Markov’s Calculus of Probabilities: Problem 3 – Solution to Parts 3, 4 and 5," Convergence (November 2023)

A Selection of Problems from A.A. Markov’s Calculus of Probabilities