In frequentist inference, a **likelihood function** (often simply the **likelihood**) is a function of the parameters of a statistical model, given specific observed data. Likelihood functions play a key role in frequentist inference, especially methods of estimating a parameter from a set of statistics. In informal contexts, "likelihood" is often used as a synonym for "probability". In mathematical statistics, the two terms have different meanings. *Probability* in this mathematical context describes the plausibility of a random outcome, given a model parameter value, without reference to any observed data. *Likelihood* describes the plausibility of a model parameter value, given specific observed data.

In Bayesian inference, although one can speak about the likelihood of any proposition or random variable given another random variable: for example the likelihood of a parameter value or of a statistical model (see marginal likelihood), given specified data or other evidence,^{[1]}^{[2]}^{[3]}^{[4]} the likelihood function remains the same entity, with the additional interpretations of (i) a conditional density of the data given the parameter (since it is then a random variable) and (ii) a measure or amount of information brought by the data about the parameter value or even the model.^{[1]}^{[2]}^{[3]}^{[4]}^{[5]} Due to the introduction of a probability structure on the parameter space or on the collection of models, it is a possible occurrence that a parameter value or a statistical model have a large likelihood value for a given specified observed data, and yet have a low *probability*, or vice versa.^{[3]}^{[5]} This is often the case in medical contexts.^{[6]} Following Bayes' Rule, the likelihood when seen as a conditional density can be multiplied by the prior probability density of the parameter and then normalized, to give a posterior probability density.^{[1]}^{[2]}^{[3]}^{[4]}^{[5]}

The likelihood function is always defined as a function of the parameter equal to (or sometimes proportional to) the density of the observed data with respect to a common or reference measure, for both discrete and continuous probability distributions.

Let be a discrete random variable with probability mass function depending on a parameter . Then the function

- ,

considered as a function of , is the *likelihood function* (of ), given the outcome of the random variable . Sometimes the probability of "the value of for the parameter value " is written as *P*(*X* = *x* | *θ*); it is often written as *P*(*X* = *x*; *θ*), to emphasize that it is not a conditional probability.

Let be a random variable following an absolutely continuous probability distribution with density function depending on a parameter . Then the function

considered as a function of , is the *likelihood function* (of , given the outcome of ). Sometimes the density function for the value of for the parameter value is written as ; this should not be confused with , which should not be considered a conditional probability density.

In measure-theoretic probability theory, the density function is defined as the Radon–Nikodym derivative of the probability distribution relative to a common dominating measure, and the likelihood function is this density interpreted as a function of the parameter (possibly a vector), not of the possible outcomes. This provides a likelihood function for any probability model with all distributions, whether discrete, absolutely continuous, a mixture or something else. (Likelihoods will be comparable, e.g. for parameter estimation, only if they are Radon–Nikodym derivatives with respect to the same dominating measure.)

The discussion above of likelihood with discrete probabilities is a special case of this using the counting measure, which makes the probability of any single outcome equal to the probability density for that outcome.

Consider a simple statistical model of a coin flip, with a single parameter that expresses the "fairness" of the coin. This parameter is the probability that a given coin lands heads up ("H") when tossed. can take on any numeric value within the range 0.0 to 1.0. For a perfectly fair coin, = 0.5.

Imagine flipping a coin twice, and observing the following data : two heads in two tosses ("HH"). Assuming that each successive coin flip is IID, then the probability of observing HH is

Hence: given the observed data HH, the *likelihood* that the model parameter equals 0.5, is 0.25. Mathematically, this is written as

This is not the same as saying that the probability that , given the observation HH, is 0.25. (For that, we could apply Bayes' theorem, which implies that the posterior probability is proportional to the likelihood times the prior probability.)

Suppose that the coin is not a fair coin, but instead it has . Then the probability of getting two heads is

Hence

More generally, for each value of , we can calculate the corresponding likelihood. The result of such calculations is displayed in Figure 1.

In Figure 1, the integral of the likelihood over the interval [0, 1] is 1/3. That illustrates an important aspect of likelihoods: likelihoods do not have to integrate (or sum) to 1, unlike probabilities.

For many applications, the natural logarithm of the likelihood function, called the **log-likelihood**, is more convenient to work with. This is because we are generally interested in where the likelihood reaches its maximum value: the logarithm is a strictly increasing function, so the logarithm of a function achieves its maximum value at the same points as the function itself, and hence the log-likelihood can be used in place of the likelihood in maximum likelihood estimation and related techniques. Finding the maximum of a function often involves taking the derivative of a function and solving for the parameter being maximized, and this is often easier when the function being maximized is a log-likelihood rather than the original likelihood function, because the probability of the conjunction of several independent variables is the product of probabilities of the variables and solving an additive equation is usually easier than a multiplicative one.

For example, some likelihood functions are for the parameters that explain a collection of statistically independent observations. In such a situation, the likelihood function factors into a product of individual likelihood functions. The logarithm of this product is a sum of individual logarithms, and the derivative of a sum of terms is often easier to compute than the derivative of a product. In addition, several common distributions have likelihood functions that contain products of factors involving exponentiation. The logarithm of such a function is a sum of products, again easier to differentiate than the original function.

The gamma distribution has two parameters, and . The likelihood function is

Finding the maximum likelihood estimate of for a single observed value looks rather daunting. Its logarithm is much simpler to work with:

To maximize the log-likelihood, we first take the partial derivative with respect to :

If there are a number of independent observations , then the joint log-likelihood will be the sum of individual log-likelihoods, and the derivative of this sum will be a sum of derivatives of each individual log-likelihood:

To complete the maximization procedure for the joint log-likelihood, the equation is set to zero and solved for :

Here denotes the maximum-likelihood estimate, and is the sample mean of the observations.

Among many applications, we consider here one of broad theoretical and practical importance. Given a parameterized family of probability density functions (or probability mass functions in the case of discrete distributions)

where is the parameter, the **likelihood function** is

written

where is the observed outcome of an experiment. In other words, when is viewed as a function of with fixed, it is a probability density function, and when viewed as a function of with fixed, it is a likelihood function.

This is not the same as the probability that those parameters are the right ones, given the observed sample. Attempting to interpret the likelihood of a hypothesis given observed evidence as the probability of the hypothesis is a common error, with potentially disastrous consequences in medicine, engineering or jurisprudence. See prosecutor's fallacy for an example of this.

From a geometric standpoint, if we consider as a function of two variables then the family of probability distributions can be viewed as a family of curves parallel to the -axis, while the family of likelihood functions is the orthogonal curves parallel to the -axis.

The use of the probability density in specifying the likelihood function above is justified as follows. Given an observation , the likelihood for the interval , where is a constant, is given by . Observe that ,

since is positive and constant. Because ,

where is the probability density function, it follows that .

The first fundamental theorem of calculus and the l'Hôpital's rule together provide that

Then

Therefore,

and so maximizing the probability density at amounts to maximizing the likelihood of the specific observation .

The above can be extended in a simple way to allow consideration of distributions which contain both discrete and continuous components. Suppose that the distribution consists of a number of discrete probability masses and a density , where the sum of all the 's added to the integral of is always one. Assuming that it is possible to distinguish an observation corresponding to one of the discrete probability masses from one which corresponds to the density component, the likelihood function for an observation from the continuous component can be dealt with in the manner shown above. For an observation from the discrete component, the likelihood function for an observation from the discrete component is simply

where is the index of the discrete probability mass corresponding to observation , because maximizing the probability mass (or probability) at amounts to maximizing the likelihood of the specific observation.

The fact that the likelihood function can be defined in a way that includes contributions that are not commensurate (the density and the probability mass) arises from the way in which the likelihood function is defined up to a constant of proportionality, where this "constant" can change with the observation , but not with the parameter .

Consider a jar containing *N* lottery tickets numbered from 1 through *N*. If you pick a ticket randomly, then you get positive integer *n*, with probability 1/*N* if *n* ≤ *N* and with probability 0 if *n* > *N*. This can be written

where the Iverson bracket [*n* ≤ *N*] is 1 when *n* ≤ *N* and 0 otherwise.
When considered a function of *n* for fixed *N*, this is the probability distribution. When considered a function of *N* for fixed *n*, this is a likelihood function. The maximum likelihood estimate for *N* is *n* (by contrast, the unbiased estimate is 2*n* − 1).

This likelihood function is not a probability distribution, because the total

is a divergent series, and so it does not equal 1.

Suppose, however, that you pick two tickets (without replacement), rather than one. Then the probability of the outcome {*n*_{1}, *n*_{2}}, where *n*_{1} < *n*_{2}, is

When considered a function of *N* for fixed *n*_{2}, this is a likelihood function. The maximum likelihood estimate for *N* is *n*_{2}. The total

is a convergent series, and so this likelihood function can be normalized into a probability distribution.

If you pick 3 or more tickets, the likelihood function has a well defined mean value, which is larger than the maximum likelihood estimate. If you pick 4 or more tickets, the likelihood function has a well defined standard deviation too.

With 2 or more tickets, the probability distributions just derived match the results from a Bayesian analysis assuming an improper, uniform prior for *N* over all positive integers. The use of improper priors is often justified by saying that the information from the data dominates the information from the prior. If only a very few tickets are available, and a precise answer is important, this can justify the work of collecting relevant information from other sources to use as an informative prior.

Suppose that the maximum likelihood estimate for θ is . Relative plausibilities of other θ values may be found by comparing the likelihoods of those other values with the likelihood of . The **relative likelihood** of θ is defined^{[7]}^{[8]}^{[9]} as 𝓛(*θ* | *x*) ∕𝓛( | *x*).

A 10% likelihood region for θ is

and more generally, a p% **likelihood region** for θ is defined^{[7]}^{[9]} to be

If θ is a single real parameter, a p% likelihood region will typically comprise an interval of real values. If the region does comprise an interval, then it is called a **likelihood interval**.^{[7]}^{[9]}^{[10]}

Likelihood intervals can be compared to confidence intervals. If θ is a single real parameter, then under certain conditions, a 14.7% likelihood interval for θ will be the same as a 95% confidence interval.^{[7]} In a slightly different formulation suited to the use of log-likelihoods (see Wilks' theorem), the test statistic is twice the difference in log-likelihoods and the probability distribution of the test statistic is approximately a chi-squared distribution with degrees-of-freedom (df) equal to the difference in df's between the two models (therefore, the e^{−2} likelihood interval is the same as the 0.954 confidence interval; assuming difference in df's to be 1).^{[10]}

A likelihood interval can, and commonly is, used directly as an interval estimate, without claiming any particular coverage probability. As such, it differs from a confidence interval.

The relative likelihood is closely related to the likelihood ratio used in the likelihood-ratio test. The *likelihood ratio* is the ratio of any two specified likelihoods: 𝓛(*θ*_{0} | *x*) ∕𝓛(*θ*_{1} | *x*). The relative likelihood is the likelihood ratio with .^{[11]}

The definition of relative likelihood can be generalized to compare different statistical models. This generalization is based on AIC (Akaike information criterion), or sometimes AICc (Akaike Information Criterion with correction).

Suppose that, for some dataset, we have two statistical models, *M*_{1} and *M*_{2}. Also suppose that AIC(*M*_{1}) ≤ AIC(*M*_{2}). Then the **relative likelihood** of *M*_{2} with respect to *M*_{1} is defined as follows.^{[12]}

To see that this is a generalization of the earlier definition, suppose that we have some model *M* with a (possibly multivariate) parameter θ. Then for any θ, set *M*_{2} = *M*(θ), and also set . The general definition now gives the same result as the earlier definition.

In many cases, the likelihood is a function of more than one parameter but interest focuses on the estimation of only one, or at most a few of them, with the others being considered as nuisance parameters. Several alternative approaches have been developed to eliminate such nuisance parameters, so that a likelihood can be written as a function of only the parameter (or parameters) of interest: the main approaches are marginal, conditional, and profile likelihoods.^{[13]}^{[14]}

These approaches are useful because standard likelihood methods can become unreliable or fail entirely when there are many nuisance parameters or when the nuisance parameters are high-dimensional. This is particularly true when the nuisance parameters can be considered to be "missing data"; they represent a non-negligible fraction of the number of observations and this fraction does not decrease when the sample size increases. Often these approaches can be used to derive closed-form formulae for statistical tests when direct use of maximum likelihood requires iterative numerical methods. These approaches find application in some specialized topics such as sequential analysis.

Sometimes it is possible to find a sufficient statistic for the nuisance parameters, and conditioning on this statistic results in a likelihood which does not depend on the nuisance parameters.

One example occurs in 2×2 tables, where conditioning on all four marginal totals leads to a conditional likelihood based on the non-central hypergeometric distribution. This form of conditioning is also the basis for Fisher's exact test.

Sometimes we can remove the nuisance parameters by considering a likelihood based on only part of the information in the data, for example by using the set of ranks rather than the numerical values. Another example occurs in linear mixed models, where considering a likelihood for the residuals only after fitting the fixed effects leads to residual maximum likelihood estimation of the variance components.

When the likelihood function depends on many parameters, depending on the application, we might be interested in only a subset of these parameters. It is often possible to reduce the number of the uninteresting (nuisance) parameters by writing them as functions of the parameters of interest. For example, the functions might be the value of the nuisance parameter which maximizes the likelihood given the value of the other (interesting) parameters.

This procedure is called concentration of the parameters and results in the concentrated likelihood function,^{[15]} also occasionally known as the maximized likelihood function, but most often called the **profile likelihood** function. It is then possible (and simpler) to find the values of the parameters which maximizes the profile likelihood function (similar to the maximum likelihood).

For example, consider a regression analysis model with normally distributed errors. The most likely value of the error variance is the variance of the residuals. The residuals depend on all other parameters. Hence the variance parameter can be written as a function of the other parameters.

Unlike conditional and marginal likelihoods, profile likelihood methods can always be used, even when the profile likelihood cannot be written down explicitly. However, the profile likelihood is not a true likelihood, as it is not based directly on a probability distribution, and this leads to some less satisfactory properties. Attempts have been made to improve this, resulting in modified profile likelihood.

The idea of profile likelihood can also be used to compute confidence intervals that often have better small-sample properties than those based on asymptotic standard errors calculated from the full likelihood. In the case of parameter estimation in partially observed systems, the profile likelihood can be also used for identifiability analysis.^{[16]}
Results from profile likelihood analysis can be incorporated in uncertainty analysis of model predictions.^{[17]}

A partial likelihood is an adaption of the full likelihood such that only a part of the parameters (the parameters of interest) occur in it.^{[18]} It is a key component of the proportional hazards model: using a restriction on the hazard function, the likelihood does not contain the shape of the hazard over time.

The term "likelihood" has been in use in English since at least late Middle English.^{[19]} Its formal use to refer to a specific function in mathematical statistics was proposed by Ronald Fisher,^{[20]} in two research papers published in 1921^{[21]} and 1922.^{[22]} The 1921 paper introduced what is today called a "likelihood interval"; the 1922 paper introduced the term "method of maximum likelihood". Quoting Fisher:

[I]n 1922, I proposed the term ‘likelihood,’ in view of the fact that, with respect to [the parameter], it is not a probability, and does not obey the laws of probability, while at the same time it bears to the problem of rational choice among the possible values of [the parameter] a relation similar to that which probability bears to the problem of predicting events in games of chance. . . .Whereas, however, in relation to psychological judgment, likelihood has some resemblance to probability, the two concepts are wholly distinct. . . .”

^{[23]}

The 1921 paper introduced what is today called a "likelihood interval"; the 1922 paper introduced the term "method of maximum likelihood". Fisher's invention of statistical likelihood was in reaction against an earlier form of reasoning called inverse probability.^{[24]} Fisher's use of the term "likelihood" fixed the meaning of the term within mathematical statistics.

A. W. F. Edwards established the axiomatic basis for use of the log-likelihood ratio as a measure of relative **support** for one hypothesis against another.^{[25]} The **support function** is then the natural logarithm of the likelihood function. Both terms are used in phylogenetics, but were not adopted in a general treatment of the topic of statistical evidence.^{[26]}

A more detailed discussion of the history of likelihood in statistics is given by the following sources.

- Hald, A. (1998),
*A History of Mathematical Statistics from 1750 to 1930*, John Wiley & Sons, ISBN 0-471-17912-4. - Hald, A. (1999), "On the history of maximum likelihood in relation to inverse probability and least squares",
*Statistical Science*,**14**(2): 214&ndash, 222, doi:10.1214/ss/1009212248, JSTOR 2676741. - Pratt, J. W. (May 1976), "F. Y. Edgeworth",
*The Annals of Statistics*,**4**(3): 501&ndash, 514, doi:10.1214/aos/1176343457, JSTOR 2958222. - Stigler, S. M. (1978), "Francis Ysidro Edgeworth, Statistician",
*Journal of the Royal Statistical Society, Series A*,**141**(3): 287&ndash, 322, doi:10.2307/2344804, JSTOR 2344804. - Stigler, S. M. (1986),
*The History of Statistics: The Measurement of Uncertainty before 1900*, Harvard University Press, ISBN 0-674-40340-1. - Stigler, S. M. (1999),
*Statistics on the Table: The History of Statistical Concepts and Methods*, Harvard University Press, ISBN 0-674-83601-4.

- Bayes factor
- Bayesian inference
- Conditional entropy
- Conditional probability
- Empirical likelihood
- Likelihood principle
- Likelihood-ratio test
- Maximum likelihood
- Principle of maximum entropy
- Pseudolikelihood
- Score (statistics)

- ^
^{a}^{b}^{c}I. J. Good:*Probability and the Weighing of Evidence*(Griffin 1950), §6.1 - ^
^{a}^{b}^{c}H. Jeffreys:*Theory of Probability*(3rd ed., Oxford University Press 1983), §1.22 - ^
^{a}^{b}^{c}^{d}E. T. Jaynes:*Probability Theory: The Logic of Science*(Cambridge University Press 2003), §4.1 - ^
^{a}^{b}^{c}D. V. Lindley:*Introduction to Probability and Statistics from a Bayesian Viewpoint. Part 1: Probability*(Cambridge University Press 1980), §1.6 - ^
^{a}^{b}^{c}A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, D. B. Rubin:*Bayesian Data Analysis*(3rd ed., Chapman & Hall/CRC 2014), §1.3 **^**H. C. Sox, M. C. Higgins, D. K. Owens:*Medical Decision Making*(2nd ed., Wiley, 2013), http://doi.org/10.1002/9781118341544, chapters 3–4- ^
^{a}^{b}^{c}^{d}Kalbfleisch, J. G. (1985),*Probability and Statistical Inference*, Springer (§9.3). **^**Azzalini, A. (1996),*Statistical Inference—Based on the likelihood*, Chapman & Hall (§1.4.2).- ^
^{a}^{b}^{c}Sprott, D. A. (2000),*Statistical Inference in Science*, Springer (chap. 2). - ^
^{a}^{b}Hudson, D. J. (1971), "Interval estimation from the likelihood function",*Journal of the Royal Statistical Society, Series B*,**33**(2): 256–262. **^**Held, L.; Bové, D. S. (2014),*Applied Statistical Inference*, Springer (§2.1).**^**Burnham K. P. & Anderson D.R. (2002),*Model Selection and Multimodel Inference*, Springer (§2.8).**^**Pawitan, Yudi (2001).*In All Likelihood: Statistical Modelling and Inference Using Likelihood*. Oxford University Press. ISBN 0-19-850765-8.**^**Wen Hsiang Wei. "Generalized Linear Model - course notes". Tunghai University, Taichung, Taiwan. pp. Chapter 5. Retrieved 2017-10-01.**^**Montoya, Jose A.; Díaz-Francés, Eloísa; Sprott, David A. (2009). "On a criticism of the profile likelihood function".*Statistical Papers*.**50**(1): 195–202. doi:10.1007/s00362-007-0056-5.**^**Raue, A; Kreutz, C; Maiwald, T; Bachmann, J; Schilling, M; Klingmüller, U; Timmer, J (2009). "Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood".*Bioinformatics*.**25**(15): 1923–29. doi:10.1093/bioinformatics/btp358. PMID 19505944.**^**Vanlier, J; Tiemann, C; Hilbers, P; van Riel, N (2012). "An integrated strategy for prediction uncertainty analysis".*Bioinformatics*.**28**(8): 1130–35. doi:10.1093/bioinformatics/bts088. PMC 3324512. PMID 22355081.**^**Cox, D. R. (1975). "Partial likelihood".*Biometrika*.**62**(2): 269&ndash, 276. doi:10.1093/biomet/62.2.269. MR 0400509.**^**"likelihood",*Shorter Oxford English Dictionary*(2007).**^**Hald, A. (1999), "On the history of maximum likelihood in relation to inverse probability and least squares",*Statistical Science*,**14**(2): 214&ndash, 222, doi:10.1214/ss/1009212248, JSTOR 2676741.**^**Fisher, R.A. (1921), "On the "probable error" of a coefficient of correlation deduced from a small sample",*Metron*,**1**: 3–32.**^**Fisher, R.A. (1922), "On the mathematical foundations of theoretical statistics",*Philosophical Transactions of the Royal Society A*,**222**(594–604): 309–368, doi:10.1098/rsta.1922.0009, JFM 48.1280.02, JSTOR 91208.**^**Klemens, Ben. Modeling with data: tools and techniques for scientific computing. Princeton University Press, 2008, p. 329.**^**Fienberg, Stephen E (1997). "Introduction to R.A. Fisher on inverse probability and likelihood".*Statist. Sci*.**12**(3): 161. doi:10.1214/ss/1030037905.**^**Edwards, A. W. F. 1972.*Likelihood.*Cambridge University Press (expanded edition, 1992, Johns Hopkins University Press). ISBN 0-8018-4443-6**^**Royall, R. (1997).*Statistical Evidence*. Chapman & Hall.

- Fraser, D. A. S.; McDunnough, P.; Naderi, A.; Plante, A. (1995), "On the definition of probability densities and sufficiency of the likelihood map" (PDF),
*Probability and Mathematical Statistics*,**15**: 301–310. - Rohde, C. A. (2014),
*Introductory Statistical Inference with the Likelihood Function*, Springer.

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