Lovegrove Mathematicals

"Probabilities are likelinesses over singleton sets"

# Terminology

## 1. The Degree

• A coin is of degree 2;
• A die is of degree 6;
• A pack of cards is of degree 52;
• The days of the week are of degree 7.

The degree is the number of possibilities that something (tossing a coin; rolling a die; drawing a card) may take. To enable a coherent theory to be developed, the possibilities/classes are labelled 1,2,...,N where N is the degree.

The set {1,...,N} is denoted by XN. This is the domain of definition for distributions and histograms of degree N.

## 2. Distributions

A distribution of degree N is a function f:

1. which is defined for i=1, ..., N  and
2. for which f(i)>0 for all i   and
3. for which f(1)+...+f(N)=1  .

It is usual to represent the distribution f by the ordered N-tuple ( f(1), ... ,f(N) ).

The set of all distributions of degree N is denoted by

If f∈S(N) then f is injective if [i≠j] ⇒ [f(i)≠f(j)]. The set of non-injective elements of S(N) has measure zero and so may be safely ignored: the description of various algorithms as producing injective distributions is for the sake of technical precision only, and has no practical or theoretical consequences.

## 3. Histograms

An histogram of degree N is a function h:-

1. which is defined for i=1,...,N
2. for which h(i)≥0 for all i.

The set of all histograms of degree N is denoted by H(N).

The sample size of h is ω(h)= h(1)+...+h(N).

When writing out the histogram h we normally just write out the values of the h(i) as an ordered N-tuple. For example, (1.25, 2.13, 4.87, 8.92)

### Integrams

An integram of degree N is an histogram of degree N which takes only integer values. The most important integram is the zero integram 0 for which all values are zero.

The set of all integrams of degree N is denoted by G(N).

We denote the integram g(i)=1, g(j)=0 for j≠i by ''i''N (the quote marks are part of the notation). Because the degree is normally obvious from the context, this is more usually written as

For example, if the degree is 6 then "2"="2"6=(0,1,0,0,0,0)

If g∈G(N) the Multinomial coefficient associated with g is given by

(Because the denominator contains the term g(i)! then g(i) must be an integer. Since this is the case for all i, g does have to be an integram, not just an histogram.)

## 4. Relative Frequencies

Let h∈H(N), h≠0 . Then we define RF(h) to be the relative frequencies of h, that is RF(h)(i)= h(i)/ω(h). An alternative notation for RF(h)(i) is RF(i|h), the relative frequency of i given h.

If we have a set of histograms of degree N, say {h1, ...,hK}, then:-

• Provided at least one h is not 0, we define the Mean Relative Frequency of i to be
.
An alternative name for this is the Global Relative Frequency of i.
• Provided no h is 0, we define the Mean of the Relative Frequencies of i to be

## 5. Convex and concave sets

A set, P, is convex if, no matter which two points we select in P, the straight line segment joining them is wholly in P. A set which is not convex is called concave, but many authors prefer the term non-convex.

The core (some prefer the term convex hull) of a set is its smallest convex superset. It follows from the definition that a convex set is its own core.

Core(P) is important because the mean of any number (not necessarily finite) of elements of P always lies in Core(P). In particular, if P is convex then the mean lies in P,  but if P is concave then that mean might not be in P.

This is especially important in some of the applied sciences, where it can be essential that the result of any analysis be describable in the same way as P. For example, R(N) -the set of ranked distributions of degree N- is convex, so a mean of ranked distributions will also be a ranked distributions of the same degree. On the other hand, U(N) -the set of unimodal distributions of degree N- is concave, so a mean of unimodal distributions might not be unimodal. When this is important there is the tendency to use best-fitting rather than best-estimating, since best-fitting forces a solution which has the required description even though

## 6. Symmetry

If P⊂S(N) then P is said to be (i,j)-symmetric if P contains f(i,j) whenever P contains f, where f(i,j) is that distribution which is obtained from f by interchanging f(i) and f(j).

The histogram h is (i,j)-symmetric if h(i)=h(j).

P is symmetric if it is (i,j)-symmetric for all i,j∈XN.

h is symmetric if it is (i,j)-symmetric for all i,j∈XN. That is, if it is a constant histogram.

## 7 Likeliness of an Integram

Let P be a non-empty subset of S(N), g∈G(N) and h∈H(N) then we define the Likeliness, over P, of g given h by

h is called the , and g is the required integram. P is the underlying set.

(Σ is the Daniell integral, which can be thought of as summation on a finite set but as the Riemann integral when that is required.)

Provided no confusion results, we

1. write LP(i|h) rather than LP("i"|h);
2. write LP(g) rather than LP(g|0).

The L-point is the point (distribution) in S(N) with co-ordinates

( LP(1|h), ..., LP(N|h) ). Associated with this is the function

which will be often used on this site to plot graphs. When doing this, we shall sometimes use the alternative notation Average(P) rather than LP.

## 8 Likeliness of a set of distributions

If Pis an underlying set in (ie. a non-empty subset of) S(N), V⊂S(N) and h∈H(N) then we define the Likeliness, over P, of V given h by

When h=0, we write LP(V) rather than LP(V|0)

A fundamental difference between the likeliness of an integram and the likeliness of a set of distributions is that the former cannot be 0 but the latter can (if V and P do not intersect). Similarly, the likeliness of an integram cannot be 1 (except in the degenerate case N=1), but that of a set of distributions can (if P⊂V).

If P is a singleton set, P={f}, then LP(V|h) can be only 0 (if f∉V) or 1 (if f∈V) and is equivalent to the characteristic function of V.

## 9. Probability of an integram given a distribution

If the underlying set is singleton, P={f}, then the expression for LP(g|h) takes on an especially simple and significant form, namely LP(g|h)= M(g)fg.

This is the everyday Multinomial Theorem form of the probability of g when the generating distribution is f. , we may define Pr(g|f)=L{f}(g|h) and call this the probability of the integram g given the distribution f.

This is a highly significant step, because it is defining probability in terms of likeliness, rather than the other way round.

It follows from this that Pr("i"|f) = f(i).

It is important to note that, since the expression M(g)fg makes no reference to h, Pr(g|f) is independent of h, that is of experimental/observational data.

So probabilities are likelinesses over singleton sets, and are independent of the given histogram.