Introductory university-level calculus, linear algebra, abstract algebra, probability, statistics, and stochastic processes.
Taylor series, Maclaurin series
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Suppose the function is of class in the neighbor of . Then, we can define the following power series:
where . If this power series has a positive radius of convergence, and the function defined by it matches in the neighbor of , we say the function is analytic. is called the Taylor series of at . A Taylor series at is called a Maclaurin series.
Example. Let us define the function on the open interval by the following power series
where is the radius of convergence of the power series. By Corollary 2 in Calculus of power series, . Therefore, is analytic and (Eq:eg1) gives the Taylor series. □
Let be a function of class in a neighbor of and . Suppose the following condition is satisfied:
() There exists an such that, for all and for all , if , then .
Then, is analytic at , and the radius of convergence of its Maclaurin series is at least .
Proof. Choose an such that and consider the finite Maclaurin expansion of :
For the remainder , we have (). Therefore, the above finite Maclaurin expansion converges as and the limit is equal to . Thus, is analytic at and its radius of convergence is at least . ■
Example. Let us show that the exponential function is analytic and its Maclaurin series is given as
Let . For any , let . For any such that and for any , so that is analytic at . Since is arbitrary, the radius of convergence is . For all , so that the Maclaurin series is given as in (Eq:Exp). □
Example. It is an exercise to show that the Maclaurin series of and are given as the following:
Also, show that the radii of convergence of these series are both . □
Power functions and the binomial theorem
Given and , we define the binomial coefficient by
The numerator for the case when is the product of consecutive numbers from to . For example,
Note that the binomial coefficients are defined for all .
If is a non-negative integer, then is the number of combinations when we choose elements out of (" choose ") for , or for .
Lemma
Proof. If , we have
Thus, (Eq:binsum) holds.
If ,
■
For , consider the power series
If , then this is a polynomial of degree . Otherwise, for all , and as ,
so that the radius of convergence of the power series (Eq:binpow) is 1.
See also: Power series (Theorem (Radius of convergence)).
Theorem (Binomial Theorem)
For and ,
Proof. Let be the function on defined by the right-hand side of (Eq:binser). By term-wise differentiation, we have
Using the above Lemma,
Let . We have
Thus, both and satisfies the same differential equation
Let us define the function on by
Then,
so that (constant) on . But . Thus, . ■
Remark. When , for . Therefore, in this case, we have
This is the binomial theorem you might have learned in high school. □
Exercise. Show that . □
Example. With , we have
for . This is the formula of geometric series. □
Example. Let us show that is analytic and find its Maclaurin series and radius of convergence. Applying the binomial theorem to , we have
By term-wise integration,
where is a constant. But so . Therefore
The radius of convergence of the right-hand side of (Eq:atans) is equal to that of (Eq:atanps), which is 1 (verify!). Thus, is analytic, and its radius of convergence is 1. □
List of frequently used Maclaurin series
It comes in handy if you memorize the following Maclaurin series. Make sure you can derive them.
Defining the birth process Consider a colony of bacteria that never dies. We study the following process known as the birth process , also known as the Yule process . The colony starts with cells at time . Assume that the probability that any individual cell divides in the time interval is proportional to for small . Further assume that each cell division is independent of others. Let be the birth rate. The probability of a cell division for a population of cells during is . We assume that the probability that two or more births take place in the time interval is . That is, it can be ignored. Consequently, the probability that no cell divides during is . Note that this process is an example of the Markov chain with states \({n_0}, {n_0 + 1}, {n_0 + 2}...
Generational growth Consider the following scenario (see the figure below): A single individual (cell, organism, etc.) produces descendants with probability , independently of other individuals. The probability of this reproduction, , is known. That individual produces no further descendants after the first (if any) reproduction. These descendants each produce further descendants at the next subsequent time with the same probabilities. This process carries on, creating successive generations. Figure 1. An example of the branching process. Let be the random variable representing the population size (number of individuals) of generation . In the above figure, we have , , , , We shall assume as the initial condition. Ideally, our goal would be to find how the population size grows through generations, that is, to find the probability for e...
In mathematics, we must prove (almost) everything and the proofs must be done logically and rigorously. Therefore, we need some understanding of basic logic. Here, I will informally explain some rudimentary formal logic. Definitions (Proposition): A proposition is a statement that is either true or false. "True" and "false" are called the truth values, and are often denoted and . Here is an example. "Dr. Akira teaches at UBD." is a statement that is either true or false (we understand the existence of Dr. Akira and UBD), hence a proposition. The following statement is also a proposition, although we don't know if it's true or false (yet): Any even number greater than or equal to 4 is equal to a sum of two primes. See also: Goldbach's conjecture Next, we define several operations on propositions. Note that propositions combined with these operations are again propositions. (Conjunction, logical "and"): Let ...
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