SciTech-Mathmatics-Probability+Statistics: Distinguishing(区分) Probability(attaches to Outcomes MECE) from Likelihood(attaches Hypothesis are often neither MECE)

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PRESIDENTIAL COLUMN: Bayes for Beginners: Probability and Likelihood

C. Randy Gallistel, August 31, 2015; TAGS: C. RANDY GALLISTEL COLUMNS | DATA | EXPERIMENTAL PSYCHOLOGY | METHODOLOGY | STATISTICAL ANALYSIS

Distinguishing Likelihood(可能性) From Probability(概率)

The distinction between probability and likelihood is fundamentally important:

• Probability attaches to possible outcomes(可能的产出);
  
  the possible outcomes to which probabilities attach are MECE(Mutually Exclusive and Collectively Exhaustive);
• Likelihood attaches to hypotheses(假设).
  
  the hypotheses to which likelihoods attach are often neither(MECE);

Explaining this distinction is the purpose of this first column.

Possible results are MECE

Possible outcomes are MECE(Mutually Exclusive and Collectively Exhaustive).

Suppose we ask a subject to predict the outcome of each of 10 tosses of a coin.

There are only 11 possible outcomes(0 to 10 correct predictions).

The actual result will always be one and only one of the possible outcomes.

Thus, the probabilities that attach to the possible outcomes MUST sum to 1.

Hypotheses are often neither(MECE)

Hypotheses, unlike outcomes, are neither mutually exclusive nor collectively exhaustive.

Suppose that the first subject we test predicts 7 of the 10 outcomes correctly.

• I might hypothesize that the subject just guessed,
• and you might hypothesize that the subject may be somewhat clairvoyant,
  
  by which you mean that the subject may be expected to correctly predict the results,
  
  at slightly greater than chance rates over the long run.
  
  These are different hypotheses, but they are not mutually exclusive,
  
  because you hedged when you said "may be.",
  
  You thereby allowed your hypothesis include mine.
• In technical terminology, my hypothesis is nested within yours.
  
  Someone else might hypothesize that the subject is strongly clairvoyant and that the observed result underestimates the probability that her next prediction will be correct.
  
  Another person could hypothesize something else altogether.
  
  There is no limit to the hypotheses one might entertain.

The set of hypotheses to which we attach likelihoods is limited by our capacity to dream them up. In practice, we can rarely be confident that we have imagined all the possible hypotheses. Our concern is to estimate the extent to which the experimental results affect the relative likelihood of the hypotheses we and others currently entertain. Because we generally do not entertain the full set of alternative hypotheses and because some are nested within others, the likelihoods that we attach to our hypotheses do not have any meaning in and of themselves; only the relative likelihoods — that is, the ratios of two likelihoods — have meaning.

"Forwards" and "Backwards"

The difference between probability and likelihood becomes clear,

when one uses the probability distribution function in general-purpose programming languages.

• In the present case, the function we want is the \(binomial\ distribution\ function\).
  
  It is called \(BINOM.DIST\) in the most common spreadsheet software and \(binopdf\) in the language I use. It has three input arguments: the number of successes, the number of tries, and the probability of a success. When one uses it to compute probabilities, one assumes that the latter two arguments (number of tries and the probability of success) are given. They are the parameters of the distribution. One varies the first argument (the different possible numbers of successes) in order to find the probabilities that attach to those different possible results (top panel of Figure 1). Regardless of the given parameter values, the probabilities always sum to 1.
• By contrast, in computing a likelihood function, one is given the number of successes (7 in our example) and the number of tries (10). In other words, the given results are now treated as parameters of the function one is using. Instead of varying the possible results, one varies the probability of success (the third argument, not the first argument) in order to get the binomial likelihood function (bottom panel of Figure 1). One is running the function backwards, so to speak, which is why likelihood is sometimes called reverse probability.

The information that the binomial likelihood function conveys is extremely intuitive. It says that given that we have observed 7 successes in 10 tries, the probability parameter of the binomial distribution from which we are drawing (the distribution of successful predictions from this subject) is very unlikely to be 0.1; it is much more likely to be 0.7, but a value of 0.5 is by no means unlikely. The ratio of the likelihood at p = .7, which is .27, to the likelihood at p = .5, which is .12, is only 2.28. In other words, given these experimental results (7 successes in 10 tries), the hypothesis that the subject's long-term success rate is 0.7 is only a little more than twice as likely as the hypothesis that the subject's long-term success rate is 0.5.

In summary, the likelihood function is a Bayesian basic.

To understand likelihood, you must be clear about the differences between probability and likelihood:

Probabilities attach to results; likelihoods attach to hypotheses.

In data analysis, the "hypotheses" are most often a possible value or a range of possible values for the mean of a distribution, as in our example.

• The results to which probabilities attach are MECE(Mutually Exclusive and Collectively Exhaustive);
• the hypotheses to which likelihoods attach are often neither;
  
  the range in one hypothesis may include the point in another, as in our example.
  
  To decide which of two hypotheses is more likely given an experimental result,
  
  we consider the ratios of their likelihoods. This ratio, the relative likelihood ratio, is called the "Bayes Factor".