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Credible interval

A credible interval, also known as a Bayesian credible interval, is an interval estimate for a parameter in Bayesian statistics that contains the true value with a specified posterior probability, such as 95%, given the observed data and prior beliefs.^[1]^[2] Unlike frequentist confidence intervals, which provide a long-run frequency interpretation over repeated samples, credible intervals treat the parameter as a random variable and directly quantify the probability that it falls within the interval based on the posterior distribution.^[1]^[3] In Bayesian inference, credible intervals are derived from the posterior distribution, which combines the likelihood of the data with the prior distribution on the parameter.^[4] For a parameter \theta, a $100(1 - \alpha)\% credible interval [a, b] satisfies P(a \leq \theta \leq b \mid \text{[data](/page/Data)}) = 1 - \alpha, where the probability is computed over the posterior.^[1]^[3] This probabilistic interpretation allows for intuitive statements about parameter uncertainty, such as "there is a 95% probability that the true parameter lies within this interval," which is not possible with confidence intervals.^[2] There are several types of credible intervals, with the two most common being the equal-tailed interval and the highest posterior density (HPD) interval.^[2] The equal-tailed interval is constructed by taking the central $1 - \alpha portion of the posterior, specifically the quantiles from \alpha/2 to $1 - \alpha/2, which is symmetric in probability mass but may not be the shortest possible interval.^[1]^[5] In contrast, the HPD interval consists of the set of values where the posterior density exceeds a certain threshold, ensuring it is the smallest interval (or region in multiple dimensions) with the desired coverage probability; this makes it particularly useful for skewed distributions.^[2]^[6] Both types can be computed analytically for conjugate priors or numerically via methods like Markov chain Monte Carlo for complex models.^[7] Credible intervals offer advantages in incorporating prior information and providing direct uncertainty quantification, which is especially valuable in fields like medicine, physics, and machine learning where subjective priors may reflect expert knowledge.^[4]^[8] However, their properties depend on the choice of prior, and in some cases, they may differ substantially from confidence intervals, particularly with informative priors or small sample sizes.^[2] Overall, credible intervals exemplify the Bayesian paradigm's emphasis on updating beliefs with data, enabling more flexible and interpretable inference compared to purely frequentist approaches.^[3]^[7]

Bayesian Foundations

Definition and Interpretation

In Bayesian statistics, a credible interval for an unknown parameter \theta is a range of values derived from the posterior distribution p(\theta \mid X), where X denotes the observed data, such that the probability that \theta lies within the interval equals a specified level, typically $1 - \alpha. Formally, a $100(1 - \alpha)\% credible interval is a random interval C(X) satisfying P(\theta \in C(X) \mid X) = 1 - \alpha, with the probability computed with respect to the posterior distribution updated by the data and the prior p(\theta).^[9] This interval directly quantifies the uncertainty about the parameter \theta given the data and prior beliefs, providing a probabilistic statement about the location of \theta in the parameter space after incorporating evidence from the likelihood p(X \mid \theta). Unlike approaches based on sampling distributions of estimators, credible intervals treat \theta as a random variable under the posterior, enabling interpretations such as "there is a $95\% posterior probability that \theta falls within this range" for an \alpha = 0.05 interval.^[9] One common type is the equal-tailed credible interval, which divides the posterior probability mass equally between the lower and upper tails. For a $100(1 - \alpha)\% equal-tailed interval [L, U], it satisfies

\int_{-\infty}^{L} p(\theta \mid X) \, d\theta = \frac{\alpha}{2}, \quad \int_{U}^{\infty} p(\theta \mid X) \, d\theta = \frac{\alpha}{2},

corresponding to the \alpha/2 and $1 - \alpha/2 quantiles of the posterior distribution; this construction is straightforward for symmetric posteriors and is often the default choice due to its simplicity.^[9] The term "credible interval" was coined by Edwards, Lindman, and Savage in 1963 to emphasize its basis in subjective Bayesian probabilities, distinguishing it from frequentist concepts while highlighting the interval's believability given the posterior evidence.^[10]

Role in Bayesian Inference

In Bayesian inference, the process begins with specifying a prior distribution p(\theta) that encodes beliefs about the unknown parameter \theta before observing data X. This prior is then updated using the likelihood p(X | \theta), which describes the probability of the data given the parameter, to obtain the posterior distribution p(\theta | X). Credible intervals are derived directly from this posterior, providing a range of plausible values for \theta that contains a specified probability mass, such as 95%, under the updated beliefs.^[11]^[12] The posterior is formally derived via Bayes' theorem as

p(\theta | X) = \frac{p(X | \theta) p(\theta)}{\int p(X | \theta) p(\theta) \, d\theta},

where the denominator serves as the normalizing constant, or marginal likelihood, ensuring the posterior integrates to 1. This normalization step integrates over all possible values of \theta, making the posterior a proper probability distribution. The choice of prior profoundly influences the location and width of the resulting credible intervals; for instance, informative priors can shift the interval toward expected values or narrow it by incorporating external knowledge, while non-informative priors yield intervals more driven by the data alone.^[11]^[13]^[14] A classic example of prior dependence occurs with conjugate priors, where the posterior retains the same distributional form as the prior, facilitating analytical computation. For a binomial likelihood modeling success probability \theta with n trials and k successes, a beta prior \text{Beta}(\alpha, \beta) yields a beta posterior \text{Beta}(\alpha + k, \beta + n - k), whose credible intervals can then be computed from the updated parameters, demonstrating how the prior hyperparameters \alpha and \beta adjust the interval's position and spread based on prior "pseudo-observations."^[15]^[16] Credible intervals also play a key role in Bayesian hypothesis testing and decision theory, particularly for evaluating point null hypotheses like H_0: \theta = \theta_0, where the interval's inclusion or exclusion of \theta_0 informs the posterior probability of the null. They serve as building blocks for Bayes factors, which quantify evidence for competing models by comparing posterior odds to prior odds, aiding decisions under uncertainty without relying on p-values.^[17]^[18]

Comparison to Frequentist Intervals

Confidence Intervals Overview

In frequentist statistics, a confidence interval provides a range of plausible values for an unknown parameter based on sample data. Specifically, a (1-\alpha)100\% confidence interval for a parameter \theta is defined as a random interval [L(X), U(X)], where X represents the observed data, such that the probability P(L(X) \leq \theta \leq U(X)) = 1-\alpha, with this probability taken over the sampling distribution of X conditional on the fixed true value of \theta.^[19]^[20] The frequentist interpretation of a confidence interval emphasizes long-run frequency coverage rather than a direct probability statement about the parameter for a given sample. In repeated sampling from the same population, approximately $1-\alpha proportion of the constructed intervals will contain the true value of \theta, reflecting the reliability of the procedure over many hypothetical experiments.^[21]^[22] This perspective treats \theta as fixed but unknown, and the interval as random due to variability in the sample. Confidence intervals are commonly constructed using pivot-based methods, which exploit known properties of the sampling distribution to achieve the desired coverage probability. For instance, when estimating the mean \mu of a normal distribution with known standard deviation \sigma based on a sample of size n, the (1-\alpha)100\% confidence interval is given by

\left[ \bar{X} - z_{\alpha/2} \frac{\sigma}{\sqrt{n}}, \bar{X} + z_{\alpha/2} \frac{\sigma}{\sqrt{n}} \right],

where \bar{X} is the sample mean and z_{\alpha/2} is the (1-\alpha/2) quantile of the standard normal distribution.^[23]^[24] Exact coverage of confidence intervals typically requires specific assumptions, such as known population parameters (e.g., variance) or adherence to a particular distributional form like normality, particularly for small sample sizes. For larger samples, asymptotic approximations based on the central limit theorem often suffice to ensure approximate coverage, even under milder conditions.^[25]^[26]

Philosophical and Practical Differences

The philosophical foundations of credible intervals and confidence intervals stem from contrasting views of probability and inference. In the Bayesian framework underlying credible intervals, probability represents a degree of belief about the parameter θ given the observed data, treating θ as a random variable and allowing direct probabilistic statements such as the posterior probability that θ lies within a specific interval.^[27] In contrast, the frequentist approach for confidence intervals views probability as a long-run frequency of coverage, where the interval is random and the parameter θ is fixed but unknown; thus, once computed from data, a specific confidence interval contains no probability statement about θ itself.^[2] This distinction arises from the Bayesian incorporation of prior beliefs to update to a posterior distribution, versus the frequentist reliance solely on the likelihood and sampling distribution without priors.^[28] Practically, credible intervals enable the inclusion of prior information, facilitating sequential updating of beliefs as new data arrive and yielding straightforward posterior probabilities, such as P(θ > 0 | X).^[27] Confidence intervals, however, do not incorporate priors and are designed to achieve nominal coverage (e.g., 95%) in repeated sampling over the long run, but they can exhibit poor finite-sample performance, such as uneven coverage or overly wide intervals in small samples.^[28] While the two interval types often coincide numerically in large samples with non-informative priors, discrepancies widen with informative priors or sparse data, where credible intervals may provide more intuitive summaries tailored to context-specific knowledge.^[28] A common pitfall in interpretation arises from misapplying frequentist confidence intervals as if they were Bayesian credible intervals, leading users to erroneously assign a probability (e.g., 95%) to the parameter lying within the observed interval; credible intervals inherently avoid this ambiguity by design, as their probability directly reflects the posterior belief given the data.^[2] Credible intervals are particularly suited for scenarios involving subjective uncertainty, prior expert knowledge, or ongoing data accumulation, where direct probability assessments enhance decision-making.^[27] Confidence intervals, conversely, are preferred when emphasizing objective, repeated-sampling guarantees without subjective inputs, such as in regulatory or standardized testing contexts.^[28]

Computation and Construction

Analytical Methods

Analytical methods for constructing credible intervals rely on scenarios where the posterior distribution admits a closed-form expression, typically arising from the use of conjugate priors that preserve the distributional family of the likelihood.^[29] In such cases, the credible interval can be derived directly from the quantiles or known properties of the posterior distribution without resorting to numerical approximation.^[30] A prominent example is the conjugate normal-normal model, where both the likelihood and prior for the mean parameter are normal distributions. For a normal likelihood with known variance σ² and n observations, paired with a normal prior N(μ₀, τ²), the posterior is also normal: N(μ_post, σ_post²), where μ_post is the precision-weighted average of the sample mean and prior mean, and σ_post² is the reciprocal of the total precision.^[29] The (1 - α) credible interval for the mean is then given by [μ_post - z_{α/2} σ_post, μ_post + z_{α/2} σ_post], with z_{α/2} denoting the (1 - α/2) quantile of the standard normal distribution.^[30] Other analytical cases include the inverse gamma prior for the variance in normal models with unknown variance. When the likelihood is normal with unknown σ² and the prior on σ² is inverse gamma IG(α₀, β₀), the posterior is IG(α_n, β_n), updated with the sample degrees of freedom and sum of squared residuals.^[31] Credible intervals for σ² are obtained via the quantiles of this posterior, such as the lower and upper α/2 quantiles. Similarly, for rate parameters in Poisson or exponential models, a gamma prior yields a gamma posterior, whose quantiles provide equal-tailed credible intervals; chi-squared posteriors, often equivalent to scaled gamma distributions in variance contexts, follow analogously.^[1] In these analytical settings, equal-tailed credible intervals are commonly constructed by taking the α/2 and 1 - α/2 quantiles of the posterior distribution, ensuring equal probability mass in each tail.^[32] This approach is straightforward for standard distributions like normal or gamma, though highest posterior density (HPD) intervals, which minimize length for a given coverage by including only the densest regions, offer optimality in terms of shortest interval but require more computation even analytically (detailed in subsequent sections). These methods are limited to models with conjugate priors that yield recognizable posterior forms, such as those within exponential families, where integrals for normalization and quantiles can be solved explicitly.^[29] For non-conjugate or complex models, analytical solutions become infeasible, necessitating simulation-based alternatives.^[31]

Simulation-Based Approaches

Simulation-based approaches are crucial for constructing credible intervals when the posterior distribution p(\theta \mid X) lacks a closed-form expression, which is common in complex Bayesian models with non-conjugate priors or high-dimensional parameters. These methods generate samples or approximations from the target posterior to empirically estimate the intervals, enabling inference in otherwise intractable settings. By drawing on Monte Carlo principles, they provide flexible tools for both equal-tailed and highest posterior density intervals, though they require careful assessment of convergence and accuracy. Markov Chain Monte Carlo (MCMC) methods form the cornerstone of simulation-based inference, iteratively generating correlated samples that asymptotically converge in distribution to p(\theta \mid X). The Metropolis-Hastings algorithm proposes candidate values from a Markov chain and accepts or rejects them based on the posterior ratio, while Gibbs sampling updates parameters conditionally one at a time, both ensuring ergodicity under mild conditions. After discarding an initial burn-in phase to mitigate starting value effects, credible intervals are obtained by sorting the post-burn-in samples and selecting the empirical \alpha/2 and $1 - \alpha/2 quantiles for equal-tailed intervals, with the sample size determining the precision of the approximation. This approach excels in multimodal or irregularly shaped posteriors but demands diagnostics like trace plots and Gelman-Rubin statistics to verify chain convergence.^[33] Importance sampling provides a non-iterative alternative by sampling from an easily evaluable proposal distribution q(\theta) and correcting for the mismatch via importance weights w_i = \frac{p(\theta_i \mid X)}{q(\theta_i)}, yielding a weighted Monte Carlo estimate of the posterior. The effective sample size, influenced by weight variance, guides the choice of q, and self-normalized weights enable direct approximation of posterior quantiles for credible intervals. This method proves advantageous for rare-event posteriors, such as those in reliability analysis, where targeted proposals can dramatically reduce variance compared to unweighted sampling. However, poor proposal choices can lead to weight degeneracy, necessitating techniques like resampling to stabilize estimates.^[34] Variational inference accelerates computation by positing a tractable approximating family q(\phi)(\theta), parameterized by \phi, and minimizing the Kullback-Leibler divergence to the true posterior through optimization of the evidence lower bound. Mean-field approximations assume conditional independence among parameters, simplifying the optimization to coordinate ascent or stochastic gradient methods, while more flexible structured families like those in black-box variational inference handle dependencies. Once optimized, credible intervals emerge from the quantiles or credible sets of the fitted q(\phi), offering scalability for large datasets at the cost of potential underestimation of posterior uncertainty. This technique is widely adopted in hierarchical models where MCMC proves computationally prohibitive.^[35] Practical implementation of these approaches is supported by open-source libraries. In R, the coda package processes MCMC chains from various samplers, computing credible intervals through quantile functions on the mcmc objects and integrating diagnostics for chain validation. In Python, PyMC facilitates model specification and MCMC execution via PyTensor-based samplers,^[36] with posterior traces analyzed using ArviZ utilities to derive credible intervals alongside summary statistics and visualizations. These tools streamline the workflow from sampling to interval extraction, promoting reproducible Bayesian analysis.^[37]

Applications and Examples

Univariate Parameter Example

Consider a simple Bayesian model for estimating the success probability p in a binomial experiment, where 100 successes are observed in 200 independent trials. A uniform prior distribution Beta(1,1) is placed on p, which is conjugate to the binomial likelihood, yielding a posterior distribution of Beta(101,101). The 95% equal-tailed credible interval for p is obtained by taking the 0.025 and 0.975 quantiles of this posterior Beta(101,101) distribution, resulting in approximately [0.43, 0.57]. This interval, as referenced in the analytical methods section, contains the central 95% of the posterior probability mass. The interpretation is direct: given the observed data and the uniform prior, there is a 95% posterior probability that the true success probability p lies between 0.43 and 0.57. To illustrate sensitivity to the prior choice, consider instead the non-informative Jeffreys prior Beta(0.5,0.5) for the binomial parameter, which leads to a posterior Beta(100.5,100.5).^[38] The corresponding 95% equal-tailed credible interval shifts slightly to approximately [0.43, 0.57], demonstrating that with a large sample size like 200 trials, the posterior interval is robust but still mildly influenced by the prior.

Multivariate Case Illustration

In the multivariate case, credible intervals extend to joint regions for vector-valued parameters, such as the mean vector \mu \in \mathbb{R}^p in a normal linear regression model with known variance. Consider a scenario with p=2 dimensions, where the data follow a multivariate normal likelihood y \sim N(X\mu, \Sigma) with known covariance \Sigma, and a conjugate multivariate normal prior \mu \sim N(\mu_0, \Lambda_0). This setup yields a multivariate normal posterior \mu \mid y \sim N(\mu_n, \Lambda_n), where \mu_n = \Lambda_n (\Lambda_0^{-1} \mu_0 + X^T \Sigma^{-1} y) and \Lambda_n^{-1} = \Lambda_0^{-1} + X^T \Sigma^{-1} X.^[9] The joint credible region for \mu at level $1-\alpha is an elliptical contour defined by the posterior covariance, specifically the set \{\mu : (\mu - \mu_n)^T \Lambda_n^{-1} (\mu - \mu_n) \leq \chi^2_{p,1-\alpha}\}, where \chi^2_{p,1-\alpha} is the $1-\alpha quantile of the chi-squared distribution with p degrees of freedom. This region contains $1-\alpha posterior probability and reflects the quadratic form of the multivariate normal density.^[9] Marginal credible intervals for individual components of \mu, say \mu_j, are obtained by integrating out the other parameters, resulting in univariate normal distributions from the diagonal elements of \Lambda_n. For instance, the marginal posterior for \mu_1 is N(\mu_{n1}, \Lambda_{n,11}), allowing separate $1-\alpha intervals [\mu_{n1} \pm z_{1-\alpha/2} \sqrt{\Lambda_{n,11}}], where z_{1-\alpha/2} is the standard normal quantile.^[9] Visualization of these regions highlights the impact of correlation in \Lambda_n: when components of \mu are positively correlated, the joint ellipsoid tilts along the line of correlation, making the region narrower in the direction of dependence compared to the rectangle formed by independent marginal intervals; this asymmetry underscores how correlations prevent the joint region from being simply the Cartesian product of marginals.^[9]

Properties and Extensions

Highest Posterior Density Intervals

The highest posterior density (HPD) interval is the shortest interval [L, U] such that \int_L^U p(\theta \mid X) \, d\theta = 1 - \alpha and it contains all \theta where p(\theta \mid X) \geq c for some threshold c that satisfies the coverage equation.^[39] This definition ensures that the interval captures the region of the posterior distribution with the highest probability density, excluding lower-density areas even if they contribute to the total mass.^[39] A key advantage of the HPD interval is its minimal length for the specified coverage probability, which is especially beneficial for skewed posterior distributions where equal-tailed intervals can be substantially wider.^[39] By focusing on the densest region, it provides a more concentrated summary of uncertainty, improving interpretability in Bayesian inference.^[40] For unimodal posteriors, computation involves solving for the density threshold c where the measure of the set \{\theta : p(\theta \mid X) \geq c\} equals $1 - \alpha, with L and U as the infimum and supremum of that set; this is typically achieved using numerical methods like grid search over the density or optimization techniques to minimize interval width subject to coverage.^[41] Monte Carlo approaches, such as those based on posterior samples, further facilitate estimation by sorting samples and identifying the narrowest interval containing the required mass while ensuring density ordering.^[40] The HPD interval assumes posterior unimodality to yield a single contiguous region, but in multimodal cases, it may comprise disjoint subintervals to encompass all highest-density components that collectively achieve the target coverage.^[41] This property allows the HPD to adapt to complex posterior shapes, though it requires careful numerical handling to identify multiple components accurately.^[40]

Credible Bands for Functions

In Bayesian statistics, credible bands extend the concept of credible intervals to functions of parameters, providing regions that contain the true function value with a specified posterior probability. For instance, a predictive credible interval for a future observation y^* is derived from the posterior predictive distribution p(y^* \mid X) = \int p(y^* \mid \theta) p(\theta \mid X) \, d\theta, where the interval consists of the central quantiles of this distribution, capturing uncertainty in both the parameters \theta and the new data.^[9] This approach quantifies the plausible range for predictions or transformations like contrasts between parameters. Credible bands are typically constructed via simulation methods, such as Markov chain Monte Carlo (MCMC), by drawing samples \theta^{(s)} from the posterior p(\theta \mid X), computing g(\theta^{(s)}) for the function of interest g, and obtaining empirical quantiles from the resulting samples to form equal-tailed bands. For highest posterior density (HPD) bands, the region is selected to include the values of g(\theta) with the highest posterior density until the desired probability mass is achieved, ensuring the shortest possible interval with the given coverage.^[9] In nonparametric settings with Gaussian process priors, bands are adapted to the smoothness of the function, using posterior means and quantiles of the supremum norm to bound the entire function over a domain. Applications of credible bands include prediction bands in regression models, where they visualize uncertainty around the estimated mean function across predictor values, aiding in forecasting and model assessment. In logistic regression, credible intervals for odds ratios—exponentiated linear combinations of coefficients—facilitate interpretation of relative risks, with bands reflecting posterior variability in these nonlinear transformations.^[42] Unlike credible intervals for raw parameters, bands for functions like g(\theta) often necessitate approximations for nonlinear g, such as the delta method, which estimates the posterior variance of g(\theta) as approximately [ \nabla g(\mu) ]^\top \Sigma [ \nabla g(\mu) ], where \mu and \Sigma are the posterior mean and covariance of \theta; however, full Monte Carlo sampling is preferred for exact bands to avoid approximation errors.^[13]

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