Frank Ramsey's essay “Truth and Probability” represents the culmination of a long tradition at Cambridge of philosophical investigation into the foundations of probability and inductive inference; and in order to appreciate completely both the intellectual context within which Ramsey wrote, and the major advance that his essay represents, it is essential to have some understanding of his predecessors at Cambridge. One of the primary purposes of this paper is to give the reader some sense of that background, identifying some of the principal personalities involved and the nature of their respective contributions; the other is to discuss just how successful Ramsey was in his attempt to construct a logic of partial belief.

“Truth and Probability” has a very simple structure. The first two sections of the essay discuss the two most important rival theories concerning the nature of probability that were current in Ramsey's day, those of Venn and Keynes. The next section then presents the alternative advocated by Ramsey, the simultaneous axiomatization of utility and probability as the expression of a consistent set of preferences. The fourth section then argues the advantages of this approach; and the last section confronts the problem of inductive inference central to English philosophy since the time of Hume. The present paper has a structure parallel to Ramsey's; each section discusses the corresponding section in Ramsey's paper.

ELLIS AND VENN

Ramsey's essay begins by disposing of the frequentist and credibilist positions; that is, the two positions advocated by his Cambridge predecessors John Venn (in The Logic of Chance, 1866) and John Maynard Keynes (in his Treatise on Probability, 1921); and thus the two positions certain to be known to his audience.

R. A. Fisher was a lifelong critic of inverse probability. In the second chapter of his last book, Statistical Methods and Scientific Inference (1956), Fisher traced the history of what he saw as the increasing disaffection with Bayesian methods that arose during the second half of the nineteenth century. Fisher's account is one of the few that covers this neglected period in the history of probability, in effect taking up where Todhunter (1865) left off, and has often been cited (e.g., Passmore, 1968, page 550, n. 7 and page 551, n. 15; de Finetti, 1972, page 159; Shafer, 1976, page 25). The picture portrayed is one of gradual progress, the logical lacunae and misconceptions of the inverse methods being steadily recognized and eventually discredited.

But on reflection Fisher's portrait does not appear entirely plausible.

This chapter introduces the notion of a ring, more specifically, a commutative ring with unity. The theory of rings provides a useful conceptual framework for reasoning about a wide class of interesting algebraic structures. Intuitively speaking, a ring is an algebraic structure with addition and multiplication operations that behave like we expect addition and multiplication should. While there is a lot of terminology associated with rings, the basic ideas are fairly simple.

Definitions, basic properties, and examples

Definition 9.1. A commutative ring with unityis a set R together with addition and multiplication operations on R, such that:

1. (i) the set R under addition forms an abelian group, and we denote the additive identity by 0R;

2. (ii) multiplication is associative; that is, for all a, b, c ∈ R, we have a(bc) = (ab)c;

3. (iii) multiplication distributes over addition; that is, for all a, b, c ∈ R, we have a(b + c) = ab + ac and (b + c)a = ba + ca;

4. (iv) there exists a multiplicative identity; that is, there exists an element 1R ∈ R, such that 1R · a = a = a · 1R for all a ∈ R;

5. (v) multiplication is commutative; that is, for all a, b ∈ R, we have ab = ba.

There are other, more general (and less convenient) types of rings–one can drop properties (iv) and (v), and still have what is called a ring.

This chapter introduces the notion of an abelian group. This is an abstraction that models many different algebraic structures, and yet despite the level of generality, a number of very useful results can be easily obtained.

Definitions, basic properties, and examples

Definition 8.1.An abelian group is a set G together with a binary operation ⋆ on G such that

1. (i) for all a, b, c ∈ G, a ⋆ (b ⋆ c) = (a ⋆ b) ⋆ c (i.e., ⋆ is associative),

2. (ii) there exists e ∈ G (called the identity element) such that for all a ∈ G, a ⋆ e = a = e ⋆ a,

3. (iii) for all a ∈ G there exists a′ ∈ G (called the inverse of a) such that a ⋆ a′ = e = a′ ⋆ a,

4. (iv) for all a, b ∈ G, a ⋆ b = b ⋆ a (i.e., ⋆ is commutative).

While there is a more general notion of a group, which may be defined simply by dropping property (iv) in Definition 8.1, we shall not need this notion in this text. The restriction to abelian groups helps to simplify the discussion significantly. Because we will only be dealing with abelian groups, we may occasionally simply say “group” instead of “abelian group.”

In this chapter, we discuss Euclid's algorithm for computing greatest common divisors. It turns out that Euclid's algorithm has a number of very nice properties, and has applications far beyond that purpose.

The basic Euclidean algorithm

We consider the following problem: given two non-negative integers a and b, compute their greatest common divisor, gcd(a, b). We can do this using the well-known Euclidean algorithm, also called Euclid's algorithm.

The basic idea of Euclid's algorithm is the following. Without loss of generality, we may assume that a ≥ b ≥ 0. If b = 0, then there is nothing to do, since in this case, gcd(a, 0) = a. Otherwise, if b > 0, we can compute the integer quotient q ≔ └a/b┘ and remainder r ≔ a mod b, where 0 ≤ r < b. From the equation

it is easy to see that if an integer d divides both b and r, then it also divides a; likewise, if an integer d divides a and b, then it also divides r. From this observation, it follows that gcd(a, b) = gcd(b, r), and so by performing a division, we reduce the problem of computing gcd(a, b) to the “smaller” problem of computing gcd(b, r).

In this chapter, we discuss basic definitions and results concerning matrices. We shall start out with a very general point of view, discussing matrices whose entries lie in an arbitrary ring R. Then we shall specialize to the case where the entries lie in a field F, where much more can be said.

One of the main goals of this chapter is to discuss “Gaussian elimination,” which is an algorithm that allows us to efficiently compute bases for the image and kernel of an F-linear map.

In discussing the complexity of algorithms for matrices over a ring R, we shall treat a ring R as an “abstract data type,” so that the running times of algorithms will be stated in terms of the number of arithmetic operations in R. If R is a finite ring, such as ℤm, we can immediately translate this into a running time on a RAM (in later chapters, we will discuss other finite rings and efficient algorithms for doing arithmetic in them).

If R is, say, the field of rational numbers, a complete running time analysis would require an additional analysis of the sizes of the numbers that appear in the execution of the algorithm. We shall not attempt such an analysis here—however, we note that all the algorithms discussed in this chapter do in fact run in polynomial time when R = ℚ, assuming we represent rational numbers as fractions in lowest terms. Another possible approach for dealing with rational numbers is to use floating point approximations.

This chapter concerns itself with the question: how many primes are there? In Chapter 1, we proved that there are infinitely many primes; however, we are interested in a more quantitative answer to this question; that is, we want to know how “dense” the prime numbers are.

This chapter has a bit more of an “analytical” flavor than other chapters in this text. However, we shall not make use of any mathematics beyond that of elementary calculus.

Chebyshev's theorem on the density of primes

The natural way of measuring the density of primes is to count the number of primes up to a bound x, where x is a real number. For a real number x ≥ 0, the function π(x) is defined to be the number of primes up to x. Thus, π(1) = 0, π(2) = 1, π(7.5) = 4, and so on. The function π is an example of a “step function,” that is, a function that changes values only at a discrete set of points. It might seem more natural to define π only on the integers, but it is the tradition to define it over the real numbers (and there are some technical benefits in doing so).

Let us first take a look at some values of π(x). Table 5.1 shows values of π(x) for x = 103i and i = 1, …, 6.

In this chapter, we review standard asymptotic notation, introduce the formal computational model we shall use throughout the rest of the text, and discuss basic algorithms for computing with large integers.

Asymptotic notation

We review some standard notation for relating the rate of growth of functions. This notation will be useful in discussing the running times of algorithms, and in a number of other contexts as well.

Suppose that x is a variable taking non-negative integer or real values, and let g denote a real-valued function in x that is positive for all sufficiently large x; also, let f denote any real-valued function in x. Then

• f = O(g) means that |f(x)| ≤ cg(x) for some positive constant c and all sufficiently large x (read, “f is big-O of g”),

• f = Ω(g) means that f(x) ≥ cg(x) for some positive constant c and all sufficiently large x (read, “f is big-Omega of g”),

• f = ⊗(g) means that cg(x) ≤ f(x) ≤ dg(x), for some positive constants c and d and all sufficiently large x (read, “f is big-Theta of g”),

• f = o(g) means that f/g → 0 as x → ∞ (read, “f is little-o of g”), and

• f ∼ g means that f/g → 1 as x → ∞ (read, “f is asymptotically equal to g”).

Example 3.1. Let f(x) ≔ x2 and g(x) ≔ 2x2 - x+1. Then f = O(g) and f = Ω(g). Indeed, f = ⊗(g). ▪