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Stretched exponential function

The stretched exponential function, also known as the Kohlrausch–Williams–Watts (KWW) function, is a phenomenological mathematical model defined by the equation \phi(t) = A \exp\left[ -\left( \frac{t}{\tau} \right)^\beta \right], where A is an amplitude, \tau is a characteristic time constant, t is time, and $0 < \beta \leq 1 is the stretching exponent that controls the deviation from simple exponential decay.^[1] This form captures slower-than-exponential relaxation tails, arising mathematically from a continuous superposition of exponential decays with a broad distribution of relaxation times, and exhibits properties such as a derivative singularity near t = 0 and infinite moments for certain transforms when \beta < 1.^[1] Unlike the standard exponential function (\beta = 1), the stretched variant is empirical yet versatile for describing non-Debye relaxation in heterogeneous systems.^[2] The function's origins trace back to 1854, when Rudolf Kohlrausch introduced it to empirically fit the residual charge decay in a Leyden jar capacitor insulated with a glass plate, marking the first documented use of such a non-exponential form for transient phenomena.^[1] It was later applied by Friedrich Kohlrausch in 1863 to mechanical relaxation in silk and by Alfred Werner in 1907 to luminescence decay, but gained renewed prominence in the 20th century.^[1] In 1970, G. Williams and D. C. Watts formalized its use in dielectric spectroscopy, proposing it as a simple empirical decay to explain non-symmetrical relaxation in organic glasses and polymers, which led to its widespread adoption as the KWW function.^[3] In physics and materials science, the stretched exponential function is prominently applied to model relaxation processes in disordered and amorphous systems, including dielectric responses in glasses, viscoelasticity in polymers, and charge transport in semiconductors.^[1] For instance, in three-dimensional glassy materials, trapping models predict \beta \approx 0.6, reflecting spatial heterogeneity.^[1] Its utility extends to luminescence decay, photoconductivity, and even non-physical domains like urban population dynamics or online content views, though limitations arise in cases requiring multiple components or precise short-time behavior.^[1] Despite its empirical nature, the function's Fourier transform relates to stable distributions in mathematics, underscoring its interdisciplinary appeal.^[1]

Definition and Formulation

Standard Form

The stretched exponential function, also known as the Kohlrausch–Williams–Watts (KWW) function or the Kohlrausch function, is defined in its standard form as

f(t) = \exp\left( -\left( \frac{t}{\tau} \right)^\beta \right),

where t \geq 0 is the independent variable, typically representing time, \tau > 0 denotes the characteristic time scale, and $0 < \beta \leq 1 is the stretching exponent that controls the shape of the decay.^[4] This empirical form was originally introduced by Rudolf Kohlrausch in 1854 to describe the discharge of a capacitor and later applied by his son Friedrich Kohlrausch in 1863 to mechanical creep in materials, and by George Williams and David C. Watts in 1970 to model non-Debye dielectric relaxation.^[1]^[3] When \beta = 1, the function reduces to the conventional exponential decay f(t) = \exp(-t/\tau), which exhibits a constant decay rate.^[4] For $0 < \beta < 1, the decay becomes slower than exponential, particularly in the long-time regime, resulting in a "stretched" tail that captures subdiffusive or anomalous relaxation behaviors observed in disordered systems.^[4] The function is restricted to the non-negative real domain t \geq 0, where it evaluates to f(0) = 1 at the origin, reflecting an initial undepleted state, and monotonically approaches 0 as t \to \infty, indicating complete relaxation over infinite time.^[4]

Parameters and Normalization

The parameter \tau in the stretched exponential function serves as the central time constant, providing the characteristic timescale over which the decay process occurs. It scales the time variable in the exponent, effectively setting the location of the decay relative to the origin. In physical contexts, \tau often corresponds to a median relaxation time for values of the stretching exponent \beta between 0.5 and 1, though it is not the arithmetic mean of the underlying relaxation times.^[1] The stretching exponent \beta, constrained to $0 < \beta \leq 1, governs the curvature of the decay, with \beta = 1 recovering the standard exponential function and smaller values producing a "stretched" profile that deviates toward slower long-time tails. Physically, \beta < 1 signals underlying heterogeneity or disorder in the system, such as a broad distribution of relaxation rates arising from traps or barriers in materials like glasses. Mathematically, it modulates the effective decay rate, which decreases as t^{\beta-1} over time.^[1] In empirical applications, the function is often written with a prefactor A \exp\left( -(t/\tau)^\beta \right), where A represents the initial amplitude or total strength of the signal at t=0, allowing fits to data with varying overall scales without altering the shape parameters \tau and \beta. This form is particularly useful in spectroscopy and relaxation experiments to isolate the dynamic behavior.^[1] For normalization as a survival or relaxation function starting at 1, the integral \int_0^\infty \exp\left( -(t/\tau)^\beta \right) \, dt = \tau \, \Gamma\left(1 + \frac{1}{\beta}\right), where \Gamma is the gamma function; this quantity equals the mean lifetime in probabilistic interpretations. When interpreted probabilistically, the stretched exponential corresponds to the complementary cumulative distribution function of the Weibull distribution, whose probability density function is the normalized form p(t) = \frac{\beta}{\tau} \left( \frac{t}{\tau} \right)^{\beta-1} \exp\left( -\left( \frac{t}{\tau} \right)^\beta \right) for t \geq 0, integrating to 1.^[5]^[6]

Mathematical Properties

Moments and Statistical Measures

The stretched exponential function, when interpreted as the survival function S(t) = \exp\left( -\left( \frac{t}{\tau} \right)^\beta \right) for t \geq 0, $0 < \beta \leq 1, and \tau > 0, corresponds to the cumulative distribution function of a Weibull random variable. The associated probability density function is f(t) = \frac{\beta}{\tau} \left( \frac{t}{\tau} \right)^{\beta - 1} \exp\left( -\left( \frac{t}{\tau} \right)^\beta \right), which is normalized such that \int_0^\infty f(t) \, dt = 1.^[7] The n-th moment of this distribution, \langle t^n \rangle = \int_0^\infty t^n f(t) \, dt, evaluates to

\langle t^n \rangle = \tau^n \Gamma\left(1 + \frac{n}{\beta}\right),

where \Gamma denotes the gamma function, and this holds for n > -\beta. For non-negative integer n, all moments are finite given \beta > 0. This expression arises from a substitution u = (t/\tau)^\beta, transforming the integral into a gamma function form.^[7] The mean lifetime is the first moment:

\langle t \rangle = \tau \Gamma\left(1 + \frac{1}{\beta}\right).

As \beta \to 0^+, $1/\beta \to \infty, and \Gamma(1 + 1/\beta) diverges, reflecting increasingly heavy-tailed behavior in the limit of extreme stretching. The variance follows from the second moment:

\text{Var}(t) = \tau^2 \left[ \Gamma\left(1 + \frac{2}{\beta}\right) - \left( \Gamma\left(1 + \frac{1}{\beta}\right) \right)^2 \right].

Higher moments are similarly obtained via the gamma function, with \beta < 1 yielding broader distributions compared to the exponential case (\beta = 1), as the coefficient of variation \sqrt{\text{Var}(t)} / \langle t \rangle > 1 and increases with decreasing \beta.^[7] In contrast, for the pure exponential distribution (\beta = 1), the moments simplify to \langle t^n \rangle = n! \, \tau^n, since \Gamma(1 + n) = n!. This factorial form highlights the narrower tail and Poisson-like moment structure of the exponential, whereas the gamma function in the stretched case captures the power-law-like broadening for \beta < 1, leading to super-Poissonian fluctuations in higher moments.^[7]

Asymptotic Expansions

The stretched exponential function f(t) = \exp\left( -\left( \frac{t}{\tau} \right)^\beta \right), with $0 < \beta \leq 1 and characteristic time \tau > 0, admits a Taylor series expansion around t = 0, obtained by substituting the argument into the standard exponential series.

f(t) = \sum_{k=0}^{\infty} \frac{(-1)^k}{k!} \left( \frac{t}{\tau} \right)^{k \beta}.

This expansion is valid for all t \geq 0 and highlights the initial non-analytic behavior for \beta < 1, where the leading correction term scales as t^\beta rather than linearly in t.^[8] For large t \gg \tau, the function exhibits subexponential decay, approaching zero more slowly than a pure exponential (\beta = 1) but more rapidly than any power-law form t^{-\gamma} for finite \gamma > 0. This intermediate decay rate distinguishes the stretched exponential from both conventional exponential relaxation and algebraic tails observed in critical phenomena.^[9] In the limit of small \beta \to 0^+, the stretched exponential approximates a power-law decay f(t) \approx e^{-1} \left( \frac{t}{\tau} \right)^{-\beta} over an intermediate range of t where \beta \ln(t/\tau) \ll 1, manifesting as a straight line with slope -\beta in a log-log plot. This limiting behavior arises from the series expansion (t/\tau)^\beta \approx 1 + \beta \ln(t/\tau), bridging stretched exponential and power-law regimes in applications like superstatistical models.^[10] Within the context of fractional calculus and relaxation equations involving fractional derivatives, the stretched exponential serves as the short-time asymptotic form (t \to 0) of the Mittag-Leffler function E_\beta(- (t/\tau)^\beta ) \sim \exp\left[ -(t/\tau)^\beta / \Gamma(1 + \beta) \right], while the full Mittag-Leffler function transitions to a power-law tail \sim (t/\tau)^{-\beta} / \Gamma(1 - \beta) at long times.^[11]

Transforms and Generating Functions

The Laplace transform of the stretched exponential function f(t) = \exp\left( -(t/\tau)^\beta \right), where $0 < \beta \leq 1 and \tau > 0, is defined as

\mathcal{L}\{ f(t) \}(s) = \int_0^\infty e^{-s t} \exp\left( -(t/\tau)^\beta \right) \, dt.

This integral lacks an elementary closed form for general \beta, but it can be expressed using the Fox H-function. A standard representation is

\mathcal{L}\{ f(t) \}(s) = \tau H_{1,1}^{1,1} \left( (s \tau)^{-\beta} \,\Bigg|\, \begin{array}{c} (1, \beta) \\ (1/\beta, 1) \end{array} \right),

where the Fox H-function provides a Mellin-Barnes integral representation suitable for asymptotic analysis and numerical evaluation.^[12] Alternatively, a series expansion is available for practical computation:

\mathcal{L}\{ f(t) \}(s) = \frac{1}{s} \sum_{k=0}^\infty \frac{(-1)^k \Gamma(\beta k + 1)}{k!} (s \tau)^{-\beta k},

valid for \Re(s) > 0, which reduces the evaluation to gamma functions and facilitates high-precision calculations in applications like dielectric spectroscopy.^[8] For the probability density function p(t) associated with the stretched exponential as a survival function—specifically, the Weibull density p(t) = \frac{\beta}{\tau} \left( \frac{t}{\tau} \right)^{\beta - 1} \exp\left( -(t/\tau)^\beta \right)—the characteristic function (Fourier transform) is

\hat{p}(k) = \int_0^\infty e^{i k t} p(t) \, dt.

This transform has no elementary closed form. Since all moments are finite, for small |k|, it expands via cumulants as \hat{p}(k) \approx 1 + i \langle t \rangle k - \frac{1}{2} \text{Var}(t) k^2 + O(k^3). The full expression requires numerical methods or series approximations, similar to the Laplace case, due to the absence of elementary forms. The ordinary generating function for the moments of the associated distribution relates directly to the Laplace transform, as the n-th moment \mathbb{E}[T^n] is obtained from the n-th derivative: \mathbb{E}[T^n] = (-1)^n \frac{d^n}{ds^n} \mathcal{L}\{ p(t) \}(s) \big|_{s=0}, linking transform properties to statistical measures without recomputing integrals. Computing these transforms poses numerical challenges, as the non-elementary nature leads to slow convergence in series for certain \beta and requires specialized techniques like double-exponential quadrature or optimized libraries (e.g., libkww) to achieve accuracies better than $10^{-7} while avoiding overflow in gamma function evaluations.^[8]

Physical and Probabilistic Interpretations

Superposition Representation

The stretched exponential function can be interpreted as a continuous superposition of simple exponential decays, arising from a distribution of relaxation rates in heterogeneous systems. This representation expresses the function as

\exp\left(-\left(\frac{t}{\tau}\right)^\beta\right) = \int_0^\infty \exp(-u t) \, \rho(u) \, du,

where \rho(u) denotes the probability density function of the rates u > 0.^[13] This form models the overall relaxation as an average over many parallel channels, each decaying exponentially with its own rate drawn from \rho(u). In disordered materials, such as glasses or polymers, this heterogeneity reflects spatial or energetic variations that lead to a broad spectrum of time constants, producing non-exponential decay observed experimentally.^[14] The density \rho(u) is obtained via the inverse Laplace transform of the stretched exponential and admits a series expansion:

\rho(u) = \frac{1}{\pi u} \Im \left[ \sum_{k=0}^\infty \frac{(-1)^k}{k!} (u \tau^\beta)^{-\beta k} \Gamma(\beta k + 1) e^{i \pi \beta k} \right],

where \Im denotes the imaginary part and \Gamma is the gamma function.^[13] For $0 < \beta < 1, \rho(u) typically exhibits a power-law divergence at small u (slow rates) and an exponential cutoff at large u (fast rates), capturing the wide range of timescales in disordered dynamics. The derivation begins by recognizing the integral as the Laplace transform \mathcal{L}\{\rho\}(t) = \exp\left(-\left(t/\tau\right)^\beta\right); inverting this transform yields \rho(u) through contour integration in the complex plane, often approximated by the Post-Widder formula or Bromwich integral for numerical evaluation.^[13] Ambiguities arise in specifying \rho(u), particularly between its integral representation (as a Stieltjes transform) and attempts to model it differentially, such as via fractional differential equations, which may not uniquely recover the density due to ill-posedness in the inversion for noisy data or boundary behaviors.^[13] However, the physical validity of this superposition as arising from independent heterogeneous relaxors remains debated, with some models favoring cooperative mechanisms in glassy dynamics.^[15] This superposition framework provides a probabilistic lens for understanding stretched exponential relaxation without invoking specific microscopic mechanisms, though it connects to applications like dielectric response in amorphous solids.^[13]

Relation to Extreme Value Distributions

The stretched exponential function appears as the survival function of the Weibull distribution, a continuous probability distribution widely used in reliability engineering and survival analysis. The cumulative distribution function (CDF) of a two-parameter Weibull random variable X with shape parameter k > 0 and scale parameter \lambda > 0 is given by

F(x) = 1 - \exp\left(-\left(\frac{x}{\lambda}\right)^k\right), \quad x \geq 0,

so the survival function S(x) = 1 - F(x) is precisely the stretched exponential \exp\left(-(x/\lambda)^k\right).^[16] This establishes a direct parameter mapping where the stretching exponent \beta corresponds to the Weibull shape k, and the characteristic time \tau aligns with the scale \lambda.^[16] In extreme value theory, the Weibull distribution belongs to the Type III family of extreme value distributions, which arises as the limiting distribution for the minima of independent and identically distributed (i.i.d.) random variables drawn from a parent distribution with a finite lower endpoint and power-law behavior near that bound. Specifically, for large sample sizes N, the distribution of the sample minimum M_N = \min\{X_1, \dots, X_N\} (after appropriate affine normalization) converges to a Weibull form whose survival function exhibits stretched exponential tails, reflecting the bounded support and the rate at which the parent tail approaches zero.^[6] This connection underscores the stretched exponential's role in modeling the "weakest link" failure mechanisms, where the minimum lifetime among many components follows such a tail.^[6] Unlike the Gumbel (Type I) distribution, which features double-exponential tails suitable for unbounded maxima or minima from exponentially decaying parent tails (e.g., normal or exponential distributions), or the Fréchet (Type II) distribution with power-law tails for heavy-tailed maxima (e.g., from Pareto-like parents), the Weibull (Type III) form is tailored to scenarios with bounded minima, producing stretched exponential decay that interpolates between exponential (k=1) and more sub-exponential behaviors for k < 1.^[17]^[18] In the unified generalized extreme value (GEV) framework, this corresponds to negative shape parameter \xi < 0, confirming the stretched exponential as the characteristic tail for finite-endpoint minima extremes.^[18]

Applications

In Physics and Materials Science

The Kohlrausch-Williams-Watts (KWW) function, given by \phi(t) = \exp\left(-(t/\tau)^\beta\right) where \tau is a characteristic time scale and $0 < \beta \leq 1 is the stretching exponent, serves as an empirical model for non-exponential relaxation processes in disordered physical systems.^[19] In materials science, it particularly fits dielectric relaxation in glasses and polymers, capturing the broad distribution of relaxation times arising from structural heterogeneity.^[19] In disordered materials, the stretched exponential describes luminescence decay, where photon emission rates slow over time due to energy transfer in inhomogeneous environments.^[20] For hopping transport in amorphous semiconductors, it models carrier relaxation, with the stretching parameter \beta linked to the localization radius of charge carriers in positional disorder.^[21] Topological features in glass networks, such as fractal-like connectivity, further justify its use, as geometric constraints lead to anomalous diffusion and stretched decay profiles.^[21] Specific examples include the signal decay in magnetic resonance imaging (MRI) of gliomas, where stretched exponentials better capture non-Gaussian diffusion in tumor tissues compared to monoexponential fits, reflecting microstructural heterogeneity.^[22] In photoluminescence studies of protein environments, the function empirically fits decay kinetics in disordered biophysical contexts, though primarily analyzed through physical relaxation mechanisms.^[23] Theoretical underpinnings arise from continuous-time random walks (CTRW) on fractals or trap models, where waiting time distributions with power-law tails produce stretched exponential survival probabilities for particles in trapping landscapes.^[24] In trap-limited transport, the aggregation of deep traps in disordered media yields the KWW form via geometric correlations, without invoking full heterogeneity superpositions.^[25] These models highlight how spatial disorder inherently generates the subdiffusive dynamics observed in experiments.^[26]

In Biology and Communications

The stretched exponential function finds significant application in biology for modeling fluorescence lifetime distributions in proteins, enabling insights into their structure-function relationships. In fluorescence lifetime imaging microscopy (FLIM), it effectively captures the heterogeneity of fluorophore environments in complex biological samples, such as tryptophan residues in rat tissue, by representing continuous distributions of lifetimes rather than discrete components. This approach provides superior contrast and signal-to-noise ratios compared to multi-exponential fits, reducing processing time while generating heterogeneity maps that highlight subtle structural variations in proteins.^[23] In epidemiology, modified forms of the stretched exponential function describe survival curves, accounting for the plasticity and rectangularity observed in human lifespan data. For instance, it models age-dependent shaping to fit trends in female survival for populations like Sweden and Japan, estimating maximum lifespans around 124-125 years with an age-varying exponent β(x) ≈ 7/ln(x) that transitions from near-exponential early mortality to near-zero risk at advanced ages. This parameterization captures post-1950 improvements in longevity more accurately than traditional models.^[27] In communications, the stretched exponential models wireless channel fading due to multipath interference in dense cellular networks. It represents path loss as e^{-α r^β}, where β tunes the impact of obstacles, integrated with Rayleigh fading to analyze coverage probability and area spectral efficiency; notably, coverage decreases with base station density, while efficiency plateaus at high densities unlike power-law models. This framework highlights interference dominance in ultra-dense deployments.^[28] For internet streaming, the stretched exponential distribution characterizes media access patterns, influencing buffer dynamics and caching performance. Object reference ranks across workloads like YouTube and IMDb follow this distribution, with parameters reflecting file sizes and access aging; it leads to stretched exponential queue size distributions in self-similar traffic, decaying as exp(-a_H x^{2-2H}) where H is the Hurst parameter, implying heavier tails and reduced caching efficiency compared to Zipf assumptions unless buffers are oversized.^[29]^[30] Online content popularity, such as article views, exhibits stretched exponential decay dynamics. Page views follow a modified model s(t) = s_0 exp[-(t/α)^β(t)], with β(t) < 1 decreasing over time via power-law scaling (e.g., γ ≈ 0.12-0.15), capturing initial rapid drops and long-tailed persistence better than exponential or biexponential fits.^[31] In engineering contexts like MRI, stretched exponential fitting models anomalous T1 and T2 relaxation times in biological tissues. Applied to intervertebral discs, it introduces a heterogeneity parameter correlating with proteoglycan content, enhancing detection of compositional changes relevant to contrast agent dynamics in musculoskeletal imaging.^[32] The stretched exponential also applies to socio-economic phenomena, such as distributions of urban agglomeration sizes, where it provides a better fit than power-law models for city population data across scales.^[8] Empirical fits in these domains often yield β parameters around 0.5-0.8, as seen in human survival analyses where β(u) adjusts for rectangularity and in network traffic where Weibull shape parameters (inversely related to β) describe queue tails.^[33]^[30]

History and Extensions

Historical Origins

The stretched exponential function was first introduced by Rudolf Kohlrausch in 1854 to model the discharge of a capacitor in dielectrics, where he proposed the empirical form q(t) = q_0 \exp\left(-(t/\tau)^\beta\right) to fit experimental data on the residual charge in a Leyden jar. This application marked the initial documented use of the function in physics, capturing non-exponential decay observed in electrical relaxation processes.^[34] Prior to the 20th century, empirical hints of stretched exponential behavior appeared in studies of viscoelasticity, notably in the 1925 work of Tool and Eichlin, who documented history-dependent properties in heat-treated glasses, such as variations in thermal expansion and elasticity linked to annealing paths. These observations suggested non-linear relaxation in amorphous materials but did not explicitly formulate the stretched exponential, serving instead as early phenomenological evidence in glass science. The function experienced a significant revival in the 20th century through the 1970 paper by Williams and Watts, who reintroduced it as the Kohlrausch-Williams-Watts (KWW) function to describe dielectric relaxation in polymers, emphasizing its utility for non-Debye processes and clarifying ambiguities in prefactor definitions across prior empirical fits. Key milestones in understanding its origins include the 2007 analysis clarifying Kohlrausch's 1854 contributions and dispelling common misattributions,^[35] as well as the 2021 paper by Trachenko and Zaccone, which synthesized physical mechanisms underlying the function's emergence in relaxation phenomena.^[36]

Variants and Generalizations

The compressed exponential function extends the stretched exponential form by using an exponent β > 1, resulting in decay faster than a simple exponential, as in \exp\left[-(t/\tau)^\beta\right]. This variant appears in correlation experiments on supercooled liquids, where it captures accelerated decay processes.^[37] In survival analysis, the Weibull distribution with shape parameter k > 1 produces a compressed exponential survival function S(t) = \exp\left[-(t/\lambda)^k\right], modeling scenarios with increasing hazard rates, such as time-to-failure in reliability engineering.^[38] A modified stretched exponential incorporates additional terms, such as a time-dependent exponent β(t), yielding forms like s(t) = \exp\left[-\left(t/\alpha\right)^{\beta(t)}\right], where β(t) evolves to fit non-stationary decays. This adjustment was developed to describe abnormal photoluminescence dynamics in disordered materials and rectangularization trends in human survival curves, enabling better capture of age-dependent shaping in demographic data from sources like the Human Mortality Database.^[31]^[33]^[39] Generalized forms include the q-stretched exponential from Tsallis nonextensive statistics, defined via the q-exponential [1 + (1-q)(t/\tau)^\beta]^{1/(1-q)} for q ≠ 1, which interpolates between exponential and power-law behaviors for modeling long-tailed distributions in seismic events and citation impacts.^[40]^[41] Another generalization arises in fractional calculus, where the Mittag-Leffler function E_\beta(-t^\beta) solves fractional relaxation equations like ^C D^\beta n(t) = -\tau^{-\beta} n(t), approximating stretched exponential decay at short times while transitioning to power-law tails at long times in viscoelastic media.^[42] For complex systems exhibiting heterogeneous dynamics, multi-exponential stretched functions combine multiple terms with distinct β values, often as a superposition over a broad distribution of relaxation times ρ(τ) ∝ τ^{β-1}, to represent non-uniform trapping or barrier distributions in glassy materials and disordered lattices.^[43] This approach addresses limitations of single stretched exponentials in capturing multi-scale relaxations, such as in spin-crossover materials or supercooled fluids.^[2]

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Understanding the stretched exponential decay of strained metallic ...
Oct 11, 2024 · The function is exponential, stretched exponential, or compressed exponential depending on β = 1, β < 1, or β > 1, respectively.
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Weibull Distribution: How to Model Time-to-Event Data | DataCamp
Oct 7, 2025 · The Weibull distribution is a continuous probability model for time-to-event data used in reliability engineering, survival analysis, ...
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Trends in scale and shape of survival curves - Scientific Reports
### Summary of Modified Stretched Exponential, Formula, and Applications
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A nonextensive modification of the Gutenberg–Richter law
This new modification has q -stretched exponential form and fits better with seismic data over the whole range of magnitudes. We assert that catalog ...
[40]
Tsallis q-exponential describes the distribution of scientific citations
Aug 6, 2025 · The stretched exponential function and the q-exponential distribution (which extends the Lomax distribution by allowing different support ...
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[PDF] The Ups and Downs of the Stretched Exponential vs. Mittag–Leffler's ...
Jan 10, 2022 · Characteristic functions appear to be expressed in terms of elementary functions whose asymptotics is simple. This opens new possibility to ...
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[PDF] Anomalous relaxation in complex systems: from stretched to ... - arXiv
May 7, 2007 · We review several models for stretched exponential relaxation (dif- fusion in traps, broad distribution of relaxation times, two-step re-.Missing: multi- | Show results with:multi-