As usual, we start with a random experiment modeled by a probability measure \(\P\) on an underlying sample space \(\Omega\). Recall that a random variable \(X\) for the experiment, with values in a set \( S \), is simply a function from \( \Omega \) to \( S \). Recall also that the probability distribution of \( X \) is the function that assigns probabilities to the subsets of \( S \), namely \( A \mapsto \P(X \in A) \) for \( A \subseteq S \). The nature of \( S \) plays a big role in how the probability distribution of \( X \) can be described. In this section, we consider the simplest case, when \( S \) is countable.
A random variable that takes values in a countable set \(S\) is said to have a discrete distribution.
Typically, \(S \subseteq \R^n\) for some \(n \in \N_+\), so in particular, if \(n \gt 1\), \(X\) is vector-valued. In the picture below, the blue dots are intended to represent points of positive probability.
If \( X \) has a discrete distribution, the probability density function (sometimes called the probability mass function) of \(X\) is the function \(f\) on \(S\) that assigns probabilities to the points in \(S\):
Let \( f(x) = \P(X = x) \) for \( x \in S \). Then
These properties follow from the axioms of a probability measure. First, \( f(x) = \P(X = x) \ge 0 \). Next, \( \sum_{x \in A} f(x) = \sum_{x \in A} \P(X = x) = \P(X \in A) \) for \( A \subseteq S \). Letting \( A = S \) in the last result gives \( \sum_{x \in S} f(x) = 1 \).
Property (c) is particularly important since it shows that the probability distribution of a discrete random variable is completely determined by its probability density function. Conversely, any function that satisfies properties (a) and (b) is a (discrete) probability density function, and then property (c) can be used to construct a discrete probability distribution on \(S\). Technically, \(f\) is the density of \(X\) relative to counting measure \(\#\) on \(S\). The technicalities are discussed in detail in the advanced section on absolute continuity and density functions.
As noted before, \(S\) is often a countable subset of some larger set, such as \(\R^n\) for some \(n \in \N_+\). But not always. We might want to consider a random variable with values in a deck of cards, or a set of words, or some other discrete population of objects. Of course, we can always map a countable set \( S \) one-to-one into a Euclidean space, but it might be contrived or unnatural to do so. In any event, if \( S \) is a subset of a larger space, we can always extend the probability density function \(f\), if we want, to the larger set by defining \(f(x) = 0\) for \(x \notin S\). Sometimes this extension simplifies formulas and notation. Values of \( x \) that maximize the probability density function are important enough to deserve a name.
An element \(x \in S\) that maximizes the probability density function \(f\) is called a mode of the distribution.
When there is only one mode, it is sometimes used as a measure of the center of the distribution.
A discrete probability distribution is equivalent to a discrete mass distribution, with total mass 1. In this analogy, \(S\) is the (countable) set of point masses, and \(f(x)\) is the mass of the point at \(x \in S\). Property (c) above simply means that the mass of a set \(A\) can be found by adding the masses of the points in \(A\).
For a probabilistic interpretation, suppose that we create a new, compound experiment by repeating the original experiment indefinitely. In the compound experiment, we have a sequence of independent random variables \((X_1, X_2, \ldots)\) each with the same distribution as \(X\); in statistical terms, we are sampling from the distribution of \(X\). Define \[f_n(x) = \frac{1}{n} \#\left\{ i \in \{1, 2, \ldots, n\}: X_i = x\right\} = \frac{1}{n} \sum_{i=1}^n \bs{1}(X_i = x), \quad x \in S\] This is the relative frequency of \(x\) in the first \(n\) runs. Note that for each \(x\), \(f_n(x)\) is a random variable for the compound experiment. By the law of large numbers, \(f_n(x)\) should converge to \(f(x)\), in some sense, as \(n \to \infty\). The function \(f_n\) is called the empirical probability density function, and it is in fact a (random) probability density function, since it satisfies properties (a) and (b) above. Empirical probability density functions are displayed in most of the simulation apps that deal with discrete variables.
It's easy to construct discrete probability density functions from other nonnegative functions defined on a countable set.
Suppose that \(g\) is a nonnegative function defined on a countable set \(S\). Let \[c = \sum_{x \in S} g(x)\] If \(0 \lt c \lt \infty\), then the function \(f\) defined by \(f(x) = \frac{1}{c} g(x)\) for \(x \in S\) is a discrete probability density function on \(S\).
Clearly \( f(x) \ge 0 \) for \( x \in S \). also \[ \sum_{x \in S} f(x) = \frac{1}{c} \sum_{x \in S} g(x) = \frac{c}{c} = 1 \]
Note that since we are assuming that \(g\) is nonnegative, \(c = 0\) if and only if \(g(x) = 0\) for every \(x \in S\). At the other extreme, \(c = \infty\) could only occur if \(S\) is infinite (and the infinite series diverges). When \(0 \lt c \lt \infty\) (so that we can construct the probability density function \(f\)), \(c\) is sometimes called the normalizing constant. This result is useful for constructing probability density functions with desired functional properties (domain, shape, symmetry, and so on).
The probability density function of a random variable \(X\) is based, of course, on the underlying probability measure \(\P\) on the sample space \(\Omega\). This measure could be a conditional probability measure, conditioned on a given event \(E \subseteq \Omega\) (with \(\P(E) \gt 0\)). The usual notation is \[f(x \mid E) = \P(X = x \mid E)\] The following theorem shows that, except for notation, no new concepts are involved. Therefore, all results that hold for discrete probability density functions in general have analogies for conditional discrete probability density functions.
For fixed \(E\), the function \(x \mapsto f(x \mid E)\) is a discrete probability density function. That is,
This is a consequence of the fact that \( A \mapsto \P(A \mid E) \) is a probability measure. The function \( x \mapsto f(x \mid E) \) plays the same role for the conditional probabliity measure that \( f \) does for the original probability measure \( \P \).
In particular, the event \( E \) in the previous result could be an event defined in terms of the random variable \( X \) itself.
Suppose that \(B \subseteq S\) and \(\P(X \in B) \gt 0\). The conditional probability density function of \(X\) given \(X \in B\) is \[f(x \mid X \in B) = \begin{cases} f(x) \big/ \P(X \in B), & x \in B \\ 0, & x \in B^c \end{cases}\]
This follows from the previous theorem. \( f(x \mid X \in B) = \P(X = x, X \in B) \big/ \P(X \in B) \). The numerator is \( f(x) \) if \( x \in B \) and is 0 if \( x \notin B \).
Note that \( \P(X \in B) = \sum_{x \in B} f(x) \), so the conditional probability density function in the previous result is simply \( f \) restricted to \( B \), and then normalized as discussed above.
Suppose that \(X\) is a random variable with a discrete distribution on a countable set \(S\), and that \(E \subseteq \Omega\) is an event in the experiment. Let \(f\) denote the probability density function of \(X\), and assume that \( f(x) \gt 0 \) for \( x \in S \), so that each point in \( S \) really is a possible value of \( X \). The versions of the law of total probability and Bayes' theorem given in the following theorems follow immediately from the corresponding results in the section on Conditional Probability. Only the notation is different. We start with the law of total probability.
If \(E\) is an event then \[\P(E) = \sum_{x \in S} f(x) \P(E \mid X = x)\]
Note that \(\{\{X = x\}: x \in S\}\) is a countable partition of the sample space \(\Omega\). That is, these events are disjoint and their union is the entire sample space \(\Omega\). Hence \[ \P(E) = \sum_{x \in S} \P(E \cap \{X = x\}) = \sum_{x \in S} \P(X = x) \P(E \mid X = x) = \sum_{x \in S} f(x) \P(E \mid X = x) \]
This result is useful, naturally, when the distribution of \(X\) and the conditional probability of \(E\) given the values of \(X\) are known. When we compute \(\P(E)\) in this way, we say that we are conditioning on \(X\). Note that \( \P(E) \), as expressed by the formula, is a weighted average of \( \P(E \mid X = x) \), with weight factors \( P(X = x) \), over \( x \in S \). The next result gives Bayes' Theorem, named after Thomas Bayes.
If \(E\) is an event with \(\P(E) \gt 0\) then \[f(x \mid E) = \frac{f(x) \P(E \mid X = x)}{\sum_{y \in S} f(y) \P(E \mid X = y)}, \quad x \in S\]
Note that the numerator of the fraction on the right is \( \P(X = x) \P(E \mid X = x) = \P(\{X = x\} \cap E) \). The denominator is \( \P(E) \) by the previous theorem. Hence the ratio is \( \P(X = x \mid E) = f(x \mid E) \).
Bayes' theorem is a formula for the conditional probability density function of \(X\) given \(E\). Again, it is useful when the quantities on the right are known. In the context of Bayes' theorem, the (unconditional) distribution of \(X\) is referred to as the prior distribution and the conditional distribution as the posterior distribution. Note that the denominator in Bayes' formula is \(\P(E)\) and is simply the normalizing constant for the function \(x \mapsto f(x) \P(E \mid X = x)\).
We start with some simple (albeit somewhat artificial) discrete distributions. After that, we study three special parametric models—the discrete uniform distribution, hypergeometric distributions, and Bernoulli trials. These models are very important, so when working the computational problems that follow, try to see if the problem fits one of these models. As always, be sure to try the problems yourself before looking at the answers and proofs in the text.
Let \(g(n) = n (10 - n)\) for \(n \in \{1, 2, \ldots, 9\}\).
Let \(g(n) = n^2 (10 -n)\) for \(n \in \{1, 2 \ldots, 10\}\).
Let \(g(x, y) = x + y\) for \((x, y) \in \{1, 2, 3\}^2\).
Let \(g(x, y) = x y\) for \((x, y) \in \{(1, 1), (1,2), (1, 3), (2, 2), (2, 3), (3, 3)\}\).
Consider the following game: An urn initially contains one red and one green ball. A ball is selected at random, and if the ball is green, the game is over. If the ball is red, the ball is returned to the urn, another red ball is added, and the game continues. At each stage, a ball is selected at random, and if the ball is green, the game is over. If the ball is red, the ball is returned to the urn, another red ball is added, and the game continues. Let \( X \) denote the length of the game (that is, the number of selections required to obtain a green ball). Find the probability density function of \( X \).
Note that \(X\) takes values in \(\N_+\). Using the multiplication rule for conditional probabilities, the PDF \(f\) of \(X\) is given by \[f(1) = \frac{1}{2} = \frac{1}{1 \cdot 2}, \; f(2) = \frac{1}{2} \frac{1}{3} = \frac{1}{2 \cdot 3}, \; f(3) = \frac{1}{2} \frac{2}{3} \frac{1}{4} = \frac{1}{3 \cdot 4}\] and in general, \(f(x) = \frac{1}{x (x + 1)}\) for \(x \in \N_+\). By partial fractions, \(f(x) = \frac{1}{x} - \frac{1}{x + 1}\) for \(x \in \N_+\) so we can check that \(f\) is a valid PDF: \[\sum_{x=1}^\infty \left(\frac{1}{x} - \frac{1}{x+1}\right) = \lim_{n \to \infty} \sum_{x=1}^n \left(\frac{1}{x} - \frac{1}{x+1}\right) = \lim_{n \to \infty} \left(1 - \frac{1}{n+1}\right) = 1\]
An element \(X\) is chosen at random from a finite set \(S\). The phrase at random means that all outcomes are equally likely.
The distribution in the last definition is called the discrete uniform distribution on \(S\). Many random variables that arise in sampling or combinatorial experiments are transformations of uniformly distributed variables.
Suppose that \(n\) elements are chosen at random, with replacement from a set \(D\) with \(m\) elements. Let \(\bs{X}\) denote the ordered sequence of elements chosen. Then \(\bs{X}\) is uniformly distributed on the set \(S = D^n\), and has probability density function \[f(\bs{x}) = \frac{1}{m^n}, \quad \bs{x} \in S\]
Recall that \( \#(D^n) = m^n \).
Suppose that \(n\) elements are chosen at random, without replacement from a set \(D\) with \(m\) elements. Let \(\bs{X}\) denote the ordered sequence of elements chosen. Then \(\bs{X}\) is uniformly distributed on the set \(S\) of permutations of size \(n\) chosen from \(D\), and hence has probability density function \[f(\bs{x}) = \frac{1}{m^{(n)}}, \quad \bs{x} \in S\]
Recall that the number of permutations of size \( n \) from \( D \) is \( m^{(n)} \).
Suppose that \(n\) elements are chosen at random, without replacement, from a set \(D\) with \(m\) elements. Let \(\bs{W}\) denote the unordered set of elements chosen. Then \(\bs{W}\) is uniformly distributed on the set \(T\) of combinations of size \(n\) chosen from \(D\), and hence has probability density function \[f(\bs{w}) = \frac{1}{\binom{m}{n}}, \quad \bs{w} \in T\]
Recall that the number of combinations of size \( n \) from \( D \) is \( \binom{m}{n} \).
Suppose that \(X\) is uniformly distributed on a finite set \(S\) and that \(B\) is a nonempty subset of \(S\). Then the conditional distribution of \(X\) given \(X \in B\) is uniform on \(B\).
From the general result above, the conditional probability density function of \( X \) given \( X \in B \) is \[ f(x \mid B) = \frac{f(x)}{\P(X \in B)} = \frac{1 \big/ \#(S)}{\#(B) \big/ \#(S)} = \frac{1}{\#(B)}, \quad x \in B \]
Suppose that a population consists of \(m\) objects; \(r\) of the objects are type 1 and \(m - r\) are type 0. Thus, the population is dichotomous; here are some typical examples:
A sample of \(n\) objects is chosen at random (without replacement) from the population. Recall that this means that the samples, either ordered or unordered are equally likely. Note that this probability model has three parameters: the population size \(m\), the number of type 1 objects \(r\), and the sample size \(n\). Now, suppose that we keep track of order, and let \(X_i\) denote the type of the \(i\)th object chosen, for \(i \in \{1, 2, \ldots, n\}\). Thus, \(X_i\) is an indicator variable (that is, a variable that just takes values 0 and 1).
\(\bs{X} = (X_1, X_2, \ldots, X_n) \) has probability density function \( f \) given by \[ f(x_1, x_2, \ldots, x_n) = \frac{r^{(y)} (m - r)^{(n-y)}}{m^{(n)}}, \quad (x_1, x_2, \ldots, x_n) \in \{0, 1\}^n \text{ where } y = x_1 + x_2 + \cdots + x_n \]
Recall again that the ordered samples are equally likely, and there are \( m^{(n)} \) such samples. The number of ways to select the \( y \) type 1 objects and place them in the positions where \( x_i = 1 \) is \( r^{(y)} \). The number of ways to select the \( n - y \) type 0 objects and place them in the positions where \( x_i = 0 \) is \( (m - r)^{(n - y)} \). Thus the result follows from the multiplication principle.
Note that the value of \( f(x_1, x_2, \ldots, x_n) \) depends only on \( y = x_1 + x_2 + \cdots + x_n \), and hence is unchanged if \( (x_1, x_2, \ldots, x_n) \) is permuted. This means that \((X_1, X_2, \ldots, X_n) \) is exchangeable. In particular, the distribution of \( X_i \) is the same as the distribution of \( X_1 \), so \( \P(X_i = 1) = \frac{r}{m} \). Thus, the variables are identically distributed. Also the distribution of \( (X_i, X_j) \) is the same as the distribution of \( (X_1, X_2) \), so \( \P(X_i = 1, X_j = 1) = \frac{r (r - 1)}{m (m - 1)} \). Thus, \( X_i \) and \( X_j \) are not independent, and in fact are negatively correlated.
Now let \(Y\) denote the number of type 1 objects in the sample. Note that \(Y = \sum_{i=1}^n X_i\). Any counting variable can be written as a sum of indicator variables.
\(Y\) has probability density function \( g \) given by.
\[g(y) = \frac{\binom{r}{y} \binom{m - r}{n - y}}{\binom{m}{n}}, \quad y \in \{0, 1, \ldots, n\}\]Recall again that the unordered samples of size \( n \) chosen from the population are equally likely. By the multiplication principle, the number of samples with exactly \( y \) type 1 objects and \( n - y \) type 0 objects is \( \binom{m}{y} \binom{m - r}{n - y} \). The total number of samples is \( \binom{m}{n} \).
The distribution defined by the probability density function in the last result is the hypergeometric distributions with parameters \(m\), \(r\), and \(n\). The term hypergeometric comes from a certain class of special functions, but is not particularly helpful in terms of remembering the model. Nonetheless, we are stuck with it. The set of values \( \{0, 1, \ldots, n\} \) is a convenience set: it contains all of the values that have positive probability, but depending on the parameters, some of the values may have probability 0. Recall our convention for binomial coefficients: for \( j, \; k \in \N_+ \), \( \binom{k}{j} = 0 \) if \( j \gt k \). Note also that the hypergeometric distribution is unimodal: the probability density function increases and then decreases, with either a single mode or two adjacent modes.
We can extend the hypergeometric model to a population of three types. Thus, suppose that our population consists of \(m\) objects; \(r\) of the objects are type 1, \(s\) are type 2, and \(m - r - s\) are type 0. Here are some examples:
Once again, a sample of \(n\) objects is chosen at random (without replacement). But now we need two random variables to keep track of the counts for the three types in the sample. Let \(Y\) denote the number of type 1 objects in the sample and \(Z\) the number of type 2 objects in the sample.
\((Y, Z)\) has probability density function \( h \) given by \[h(y, z) = \frac{\binom{r}{y} \binom{s}{z} \binom{m - r - s}{n - y - z}}{\binom{m}{n}}, \quad (y, z) \in \{0, 1, \ldots, n\}^2 \text{ with } y + z \le n\]
Once again, by the multiplication principle, the number of samples of size \( n \) from the population with exactly \( y \) type 1 objects, \( z \) type 2 objects, and \( n - y - z \) type 0 objects is \( \binom{r}{y} \binom{s}{z} \binom{m - r - s}{n - y - z} \). The total number of samples of size \( n \) is \( \binom{m}{n} \).
The distribution defined by the density function in the last exericse is the bivariate hypergeometric distribution with parameters \(m\), \(r\), \(s\), and \(n\). Once again, the domain given is a convenience set; it includes the set of points with positive probability, but depending on the parameters, may include points with probability 0. Clearly, the same general pattern applies to populations with even more types. However, because of all of the parameters, the formulas are not worthing remembering in detail; rather, just note the pattern, and remember the combinatorial meaning of the binomial coefficient. The hypergeometric model will be revisited later in this chapter, in the section on joint distributions and in the section on conditional distributions. The hypergeometric distribution and the multivariate hypergeometric distribution are studied in detail in the chapter on Finite Sampling Models. This chapter contains a variety of distributions that are based on discrete uniform distributions.
A Bernoulli trials sequence is a sequence \((X_1, X_2, \ldots)\) of independent, identically distributed indicator variables. Random variable \(X_i\) is the outcome of trial \(i\), and in the usual terminology of reliability, 1 denotes success while 0 denotes failure, The process is named for Jacob Bernoulli. Let \(p = \P(X_i = 1)\) denote the success parameter of the process. Note that the indicator variables in the hypergeometric model satisfy one of the assumptions of Bernoulli trials (identical distributions) but not the other (independence).
\(\bs{X} = (X_1, X_2, \ldots, X_n)\) has probability density function \( f \) given by \[f(x_1, x_2, \ldots, x_n) = p^y (1 - p)^{n - y}, \quad (x_1, x_2, \ldots, x_n) \in \{0, 1\}^n, \text{ where } y = x_1 + x_2 + \cdots + x_n\]
By definition, \( \P(X_i = 1) = p \) and \( \P(X_i = 0) = 1 - p \). Equivalently, \( \P(X_i = x) = p^x (1 - p)^{1-x} \) for \( x \in \{0, 1\} \). The formula for \( f \) then follows by independence.
Now let \(Y\) denote the number of successes in the first \(n\) trials. Note that \(Y = \sum_{i=1}^n X_i\), so we see again that a complicated random variable can be written as a sum of simpler ones. In particular, a counting variable can always be written as a sum of indicator variables.
\(Y\) has probability density function \( g \) given by \[g(y) = \binom{n}{y} p^y (1 - p)^{n-y}, \quad y \in \{0, 1, \ldots, n\}\]
From the previous result, any particular sequence of \( n \) Bernoulli trials with \( y \) successes and \( n - y \) failures has probability \( p^y (1 - p)^{n - y}\). The number of such sequences is \( \binom{n}{y} \), so the formula for \( g \) follows by the additivity of probability.
The distribution defined by the probability density function in the last theorem is called the binomial distribution with parameters \(n\) and \(p\). The distribution is unimodal: the probability density function at first increases and then decreases, with either a single mode or two adjacent modes. The binomial distribution is studied in detail in the chapter on Bernoulli Trials.
Now let \(N\) denote the trial number of the first success. Then \(N\) has probability density function \(h\) given by \[h(n) = (1 - p)^{n-1} p, \quad n \in \N_+\] \( h \) is decreasing and the mode is \( n = 1 \).
For \( n \in \N_+ \), the event \( \{N = n\} \) means that the first \( n - 1 \) trials were failures and trial \( n \) was a success. Each trial results in failure with probability \( 1 - p \) and success with probability \( p \), and the trials are independent, so \( \P(N = n) = (1 - p)^{n - 1} p \).
The distribution defined by the probability density function in the last exercise is the geometric distribution on \(\N_+\) with parameter \(p\). The geometric distribution is studied in detail in the chapter on Bernoulli Trials.
An urn contains 30 red and 20 green balls. A sample of 5 balls is selected at random, without replacement. Let \(Y\) denote the number of red balls in the sample.
In the ball and urn experiment, select sampling without replacement and set \(m = 50\), \(r = 30\), and \(n = 5\). Run the experiment 1000 times and note the agreement between the empirical density function of \(Y\) and the probability density function.
An urn contains 30 red and 20 green balls. A sample of 5 balls is selected at random, with replacement. Let \(Y\) denote the number of red balls in the sample.
In the ball and urn experiment, select sampling with replacement and set \(m = 50\), \(r = 30\), and \(n = 5\). Run the experiment 1000 times and note the agreement between the empirical density function of \(Y\) and the probability density function.
A group of voters consists of 50 democrats, 40 republicans, and 30 independents. A sample of 10 voters is chosen at random, without replacement. Let \(X\) denote the number of democrats in the sample and \(Y\) the number of republicans in the sample.
The Math Club at Enormous State University has 20 freshmen, 40 sophomores, 30 juniors, and 10 seniors. A committee of 8 club members is chosen at random, without replacement to organize \(\pi\)-day activities. Let \(X\) denote the number of freshman in the sample, \(Y\) the number of sophomores, and \(Z\) the number of juniors.
Suppose that a coin with probability of heads \(p\) is tossed repeatedly, and the sequence of heads and tails is recorded.
Suppose that a coin with probability of heads \(p = 0.4\) is tossed 5 times. Let \(Y\) denote the number of heads.
In the binomial coin experiment, set \(n = 5\) and \(p = 0.4\). Run the experiment 1000 times and note the agreement between the empirical density function of \(Y\) and the probability density function.
Suppose that a coin with probability of heads \(p = 0.2\) is tossed until heads occurs. Let \(N\) denote the number of tosses.
In the negative binomial experiment, set \(k = 1\) and \(p = 0.2\). Run the experiment 1000 times and note the agreement between the empirical density function and the probability density function.
Suppose that two fair, standard dice are tossed and the sequence of scores \((X_1, X_2)\) recorded. Let \(Y = X_1 + X_2\) denote the sum of the scores, \(U = \min\{X_1, X_2\}\) the minimum score, and \(V = \max\{X_1, X_2\}\) the maximum score.
We denote the PDFs by \(f\), \(g\), \(h_1\), \(h_2\), and \(h\) respectively.
Note that \((U, V)\) could serve as the outcome of the experiment that consists of throwing two standard dice if we did not bother to record order. Note from the previous exercise that this random vector does not have a uniform distribution when the dice are fair. The mistaken idea that this vector should have the uniform distribution was the cause of difficulties in the early development of probability.
In the dice experiment, select \(n = 2\) fair dice. Select the following random variables and note the shape and location of the probability density function. Run the experiment 1000 times. For each of the following variables, note the agreement between the empirical density function and the probability density function.
In the die-coin experiment, a fair, standard die is rolled and then a fair coin is tossed the number of times showing on the die. Let \(N\) denote the die score and \(Y\) the number of heads.
Run the die-coin experiment 1000 times. For the number of heads, note the agreement between the empirical density function and the probability density function.
Suppose that a bag contains 12 coins: 5 are fair, 4 are biased with probability of heads \(\frac{1}{3}\); and 3 are two-headed. A coin is chosen at random from the bag and tossed 5 times. Let \(V\) denote the probability of heads of the selected coin and let \(Y\) denote the number of heads.
Compare die-coin problem and bag of coins problem. In the first exercise, we toss a coin with a fixed probability of heads a random number of times. In second exercise, we effectively toss a coin with a random probability of heads a fixed number of times. In both cases, we can think of starting with a binomial distribution and randomizing one of the parameters.
In the coin-die experiment, a fair coin is tossed. If the coin lands tails, a fair die is rolled. If the coin lands heads, an ace-six flat die is tossed (faces 1 and 6 have probability \(\frac{1}{4}\) each, while faces 2, 3, 4, 5 have probability \(\frac{1}{8}\) each). Find the probability density function of the die score \(Y\).
\(f(y) = 5/24\) for \( y \in \{1,6\}\), \(f(y) = 7/24\) for \(y \in \{2, 3, 4, 5\}\)
Run the coin-die experiment 1000 times, with the settings in the previous exercise. Note the agreement between the empirical density function and the probability density function.
Suppose that a standard die is thrown 10 times. Let \(Y\) denote the number of times an ace or a six occurred. Give the probability density function of \(Y\) and identify the distribution by name and parameter values in each of the following cases:
Suppose that a standard die is thrown until an ace or a six occurs. Let \(N\) denote the number of throws. Give the probability density function of \(N\) and identify the distribution by name and parameter values in each of the following cases:
Fred and Wilma takes turns tossing a coin with probability of heads \(p \in (0, 1)\): Fred first, then Wilma, then Fred again, and so forth. The first person to toss heads wins the game. Let \(N\) denote the number of tosses, and \(W\) the event that Wilma wins.
The alternating coin tossing game is studied in more detail in the section on The Geometric Distribution in the chapter on Bernoulli trials.
Suppose that \(k\) players each have a coin with probability of heads \(p\), where \(k \in \{2, 3, \ldots\}\) and where \(p \in (0, 1)\).
The odd man out game is treated in more detail in the section on the Geometric Distribution in the chapter on Bernoulli Trials.
Recall that a poker hand consists of 5 cards chosen at random and without replacement from a standard deck of 52 cards. Let \(X\) denote the number of spades in the hand and \(Y\) the number of hearts in the hand. Give the probability density function of each of the following random variables, and identify the distribution by name:
Recall that a bridge hand consists of 13 cards chosen at random and without replacement from a standard deck of 52 cards. An honor card is a card of denomination ace, king, queen, jack or 10. Let \(N\) denote the number of honor cards in the hand.
In the most common high card point system in bridge, an ace is worth 4 points, a king is worth 3 points, a queen is worth 2 points, and a jack is worth 1 point. Find the probability density function of \(V\), the point value of a random bridge hand.
Suppose that in a batch of 500 components, 20 are defective and the rest are good. A sample of 10 components is selected at random and tested. Let \(X\) denote the number of defectives in the sample.
A plant has 3 assembly lines that produce a certain type of component. Line 1 produces 50% of the components and has a defective rate of 4%; line 2 has produces 30% of the components and has a defective rate of 5%; line 3 produces 20% of the components and has a defective rate of 1%. A component is chosen at random from the plant and tested.
Let \(D\) the event that the item is defective, and \(f(\cdot \mid D)\) the PDF of the line number given \(D\).
Recall that in the standard model of structural reliability, a systems consists of \(n\) components, each of which, independently of the others, is either working for failed. Let \(X_i\) denote the state of component \(i\), where 1 means working and 0 means failed. Thus, the state vector is \(\bs{X} = (X_1, X_2, \ldots, X_n)\). The system as a whole is also either working or failed, depending only on the states of the components. Thus, the state of the system is an indicator random variable \(V = V(\bs{X})\) that depends on the states of the components according to a structure function. In a series system, the system works if and only if every components works. In a parallel system, the system works if and only if at least one component works. In a \(k\) out of \(n\) system, the system works if and only if at least \(k\) of the \(n\) components work.
The reliability of a device is the probability that it is working. Let \(p_i = \P(X_i = 1)\) denote the reliability of component \(i\), so that \(\bs{p} = (p_1, p_2, \ldots, p_n)\) is the vector of component reliabilities. Because of the independence assumption, the system reliability depends only on the component reliabilities, according to a reliability function \(r(\bs{p}) = \P(V = 1)\). Note that when all component reliabilities have the same value \(p\), the states of the components form a sequence of \(n\) Bernoulli trials. In this case, the system reliability is, of course, a function of the common component reliability \(p\).
Suppose that the component reliabilities all have the same value \(p\). Let \(\bs{X}\) denote the state vector and \(Y\) denote the number of working components.
Suppose that we have 4 independent components, with common reliability \(p = 0.8\). Let \(Y\) denote the number of working components.
Suppose that we have 4 independent components, with reliabilities \(p_1 = 0.6\), \(p_2 = 0.7\), \(p_3 = 0.8\), and \(p_4 = 0.9\). Let \(Y\) denote the number of working components.
Suppose that \( a \gt 0 \). Define \( f \) by \[f(n) = e^{-a} \frac{a^n}{n!}, \quad n \in \N\]
The distribution defined by the probability density function in the previous exercise is the Poisson distribution with parameter \(a\), named after Simeon Poisson. Note that like the other named distributions we studied above (hypergeometric and binomial), the Poisson distribution is unimodal: the probability density function at first increases and then decreases, with either a single mode or two adjacent modes. The Poisson distribution is studied in detail in the Chapter on Poisson Processes, and is used to model the number of random points
in a region of time or space, under certain ideal conditions. The parameter \(a\) is proportional to the size of the region of time or space.
Suppose that the customers arrive at a service station according to the Poisson model, at an average rate of 4 per hour. Thus, the number of customers \(N\) who arrive in a 2-hour period has the Poisson distribution with parameter 8.
In the Poisson experiment, set \(r = 4\) and \(t = 2\). Run the simulation 1000 times and note the apparent convergence of the empirical density function to the probability density function.
Suppose that the number of flaws \(N\) in a piece of fabric of a certain size has the Poisson distribution with parameter 2.5.
Suppose that the number of raisins \(N\) in a piece of cake has the Poisson distribution with parameter 10.
Let \(g(n) = \frac{1}{n^2}\) for \(n \in \N_+\).
The distribution defined in the previous exercise is a member of the zeta family of distributions. Zeta distributions are used to model sizes or ranks of certain types of objects, and are studied in more detail in the chapter on Special Distributions.
Let \(f(d) = \log(d + 1) - \log(d) = \log\left(1 + \frac{1}{d}\right)\) for \(d \in \{1, 2, \ldots, 9\}\). (The logarithm function is the base 10 common logarithm, not the base \(e\) natural logarithm.)
\(d\) | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
---|---|---|---|---|---|---|---|---|---|
\(f(d)\) | 0.3010 | 0.1761 | 0.1249 | 0.0969 | 0.0792 | 0.0669 | 0.0580 | 0.0512 | 0.0458 |
The distribution defined in the previous exercise is known as Benford's law, and is named for the American physicist and engineer Frank Benford. This distribution governs the leading digit in many real sets of data. Benford's law is studied in more detail in the chapter on Special Distributions.
In the M&M data, let \(R\) denote the number of red candies and \(N\) the total number of candies. Compute and graph the empirical probability density function of each of the following:
We denote the PDF of \(R\) by \(f\) and the PDF of \(N\) by \(g\)
\(r\) | 3 | 4 | 5 | 6 | 8 | 9 | 10 | 11 | 12 | 14 | 15 | 20 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
\(f(r)\) | \(\frac{1}{30}\) | \(\frac{3}{30}\) | \(\frac{3}{30}\) | \(\frac{2}{30}\) | \(\frac{4}{30}\) | \(\frac{5}{30}\) | \(\frac{2}{30}\) | \(\frac{1}{30}\) | \(\frac{3}{30}\) | \(\frac{3}{30}\) | \(\frac{3}{30}\) | \(\frac{1}{30}\) |
\(n\) | 50 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 |
---|---|---|---|---|---|---|---|---|---|---|
\(g(n)\) | \(\frac{1}{30}\) | \(\frac{1}{30}\) | \(\frac{1}{30}\) | \(\frac{4}{30}\) | \(\frac{4}{30}\) | \(\frac{3}{30}\) | \(\frac{9}{30}\) | \(\frac{3}{30}\) | \(\frac{2}{30}\) | \(\frac{2}{30}\) |
\(r\) | 3 | 4 | 6 | 8 | 9 | 11 | 12 | 14 | 15 |
---|---|---|---|---|---|---|---|---|---|
\(f(r \mid N \gt 57)\) | \(\frac{1}{16}\) | \(\frac{1}{16}\) | \(\frac{1}{16}\) | \(\frac{3}{16}\) | \(\frac{3}{16}\) | \(\frac{1}{16}\) | \(\frac{1}{16}\) | \(\frac{3}{16}\) | \(\frac{2}{16}\) |
In the Cicada data, let \(G\) denotes gender, \(S\) species type, and \(W\) body weight (in grams). Compute the empirical probability density function of each of the following:
We denote the PDF of \(G\) by \(g\), the PDF of \(S\) by \(h\) and the PDF of \((G, S)\) by \(f\).