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In an ideal linear temperature-dependent destruction model with equal warming and cooling durations, normalized temperature is $1-e^{-\alpha t}$ during warming and $(1-e^{-\alpha T})e^{-\alpha (t-T)}$ during cooling. Integrating the destruction rate gives $D(T)$, the logarithm of the surviving-count reduction factor. If the endpoint destruction rate and thermal relaxation constant stay fixed, changing the temperature gap alone leaves this integral unchanged. This is a property of the stipulated model.

Tunneling through a controllable vacuum gap by Gerd Binnig and others made barrier width experimentally adjustable. Their apparatus used a pointed electrical conductor opposite a conducting plate, with fine positioning by piezoelectricity. Rubber-tube support and magnetic levitation above a cooled bowl exhibiting superconductivity suppressed vibrations; the junction itself remained near room temperature.^[ref]

The readout was electrical resistance versus displacement at a fixed electric potential difference of 60 mV of either polarity. They observed an exponential function dependence spanning four orders of magnitude. Cleaning and better vacuum increased the inferred barrier from approximately 0.6-0.7 eV to 3.2 eV. Reproducible curves and the cleaning response argued against deforming contamination layers; an appropriate slope also helped exclude an ordinary conducting contact. The paper reports residual contamination, electronics-limited range and a jump attributed to release of thermal stress; its curve origins generally do not determine absolute gap widths.^[ref]

The ideal tunnel junction model explains the slope. At fixed small electric potential difference, fixed tip geometry and approximately unchanged density of states, take an effective barrier energy $\Phi$ above the relevant Electron states and approximate the current magnitude byThe coefficient $C$ includes the applied electric potential difference, available occupied and empty quantum states, and tip geometry. A single transmission probability is not an electric current: a junction combines contributions from many Electron states in both directions, with the electric potential difference producing a net imbalance. Its tilted, material-dependent barrier is only approximated by the equal-exterior rectangular potential barrier; the exact single-particle formula above is not a complete junction model.

For the chosen model value $\Phi =4$ electronvolts, $\kappa \simeq 10.25{\mathrm{nm}}^{-1}$. Increasing separation by $0.1\mathrm{nm}$ multiplies $I$ by about $0.129$, a reduction by about $7.8$. The following diagram chooses $I(0.4\mathrm{nm})=1\mathrm{nA}$ solely for illustration; it contains no measured data and is not a fit to the 1982 curves.

Distance calibration is essential. In Scanning Tunneling Microscopy: From Birth to Adolescence by Gerd Binnig and others, the authors describe an initial inferred barrier near 1 eV. Recalibration found only half the quoted piezo displacement sensitivity and raised the inferred barrier above 4 eV. Thus an exponential trace alone was insufficient evidence for tunnelling through vacuum. The model makes the sensitivity explicit: the inferred $\Phi ={\hslash }^2(d\ln I/ds{)}^2/(8m_e)$ depends quadratically on the distance scale.^[ref]

For scanning tunnelling microscopy, feedback control adjusts the vertical position to hold electric current fixed during a lateral scan; the recorded height follows separation only when the surface is electronically homogeneous. Changes in the local density of states can also change apparent height. This limitation is already explicit in the authors' account of constant-current imaging.^[ref]

For $b>1$ and an integer $q\ge 2$, assign $x\ge 1$ the color $\lfloor {\log }_{b}x\rfloor \mathrm{mod}q$. The floor function partitions the domain into half-open geometric bins. If $1<{\log }_b(y/x)<q-1$, their bin indices differ by an integer in $\{1,\dots ,q-1\}$, so their colors differ. For example, $b=3/2$ and $q=3$ separates every ratio in $[1.9,2]$. Composing this finite coloring with another logarithm can obstruct monochromatic multiplicative configurations whose logarithms have such ratios.

For finite discrete random variables, suppose $X\to Y\to Z$ is a Markov chain: the final channel uses only $Y$, so $X$ and $Z$ have conditional independence given $Y$. In terms of the joint probability mass function,and hence $I(X;Z\mid Y)=0$. Then the two data processing inequalities areAll mutual informations here use base-two logarithms. Thus deterministic or randomized processing cannot increase the information shared with an unprocessed variable.^[ref]

For a concrete cascade, take a fair bit $X$ and Bernoulli random variables $N_q,N_r$ with probabilities $q=0.1$, $r=0.2$ of being one. Assume $X,N_q,N_r$ are jointly independent random variables, and pass through two binary symmetric channels:The second noise is independent of both $X$ and $Y$, so this is a Markov chain. Both $Y$ and $Z$ are fair bits. The net exclusive or error is one exactly when one noise flips the bit and the other does not:Using the binary entropy $h_2(t)=-t{\log }_{2}t-(1-t){\log }_2(1-t)$, the three mutual informations are fully determined:All numbers are in bits. The two losses are $I(X;Y\mid Z)\approx 0.3577507789$ and $I(Y;Z\mid X)\approx 0.1048182776$. Thus the cascade retains less information about the original source than either individual channel shares between its own input and output.

With $q=0.1$ fixed and $0\le r\le 1/2$, the effective error is $s=0.1+0.8r$. At $r=0$, $Z=Y$ and $I(X;Z)=I(X;Y)$; at $r=1/2$, $Z$ is independent of the previous variables and $I(X;Z)=I(Y;Z)=0$. The diagram plots these endpoints and the computed $r=0.2$ example.

The data processing inequality is useful for testing what a proposed decoder or data summary could possibly reveal: if its only input is $Y$, even randomized processing cannot provide more mutual information about $X$ than $Y$ already contains. The Markov chain hypothesis matters. In the exclusive or example from conditional mutual information, allowing a processor to use both $Y$ and $Z$ reveals $X$ exactly; that processor has additional input, so the conclusion for processing $Y$ alone does not apply.

The chain rule for mutual information proves both inequalities and identifies their exact gaps. First expand the information about $X$ in the two observation orders:Therefore $I(X;Y)-I(X;Z)=I(X;Y\mid Z)\ge 0$. Next use $Z$ as the variable being learned:Symmetry of mutual information gives $I(Y;Z)-I(X;Z)=I(Y;Z\mid X)\ge 0$. Both signs follow from the nonnegativity of conditional mutual information; the first gap also gives equality in data processing inequality.

A comparison using a logarithm of two powers is expressed as $10{\log }_{10}(P_2/P_1)\mathrm{dB}$. Positive loss instead uses $10{\log }_{10}(P_{\mathrm{in}}/P_{\mathrm{out}})\mathrm{dB}$. For wave amplitudes, a factor twenty is appropriate only when the corresponding power ratio equals the square of the amplitude ratio.

For $x>0$, the decimal significand is the unique $S_{10}(x)\in [1,10)$ such that $x=10^k{S}_{10}(x)$ for an integer $k$. Explicitly, $k=\lfloor {\log }_{10}x\rfloor$ and $S_{10}(x)=10^{\{{\log }_{10}x\}}$, using the floor function and the fractional part. For $0.0042$, $4.2$ and $4200$, the decimal significand is $4.2$ and the first nonzero digit is $\lfloor S_{10}(x)\rfloor =4$. In the classical terminology of logarithm tables, the logarithmic mantissa is $\{{\log }_{10}x\}$, which differs from the decimal significand $10^{\{{\log }_{10}x\}}$.

Differential entropy describes the spread of a continuous probability distribution relative to the volume scale of its coordinates. For a real random variable $X$ with probability density function $f$, use natural logarithms and writeThe convention is $0\ln 0=0$. For a finite value, require $\int f|\ln f|<\infty$; if both positive and negative contributions are infinite, the expression is undefined. Unlike a probability, a probability density can exceed one. Consequently differential entropy can be negative. Base-two logarithms give $h_2=h_e/\ln 2$, in bits instead of nats.^[ref]

This quantity does not give the exact number of bits needed to store a real number. A recording device supplies a resolution; equal-width bin entropy connects that resolution to discrete information entropy. The useful continuous quantity is what remains after removing the increasing cost of specifying finer bins. Its dependence on coordinates is visible already in differential entropy of a uniform law.

For a normal distribution with mean $m$ and positive variance ${\sigma }^2$, expand the logarithm of its probability density function:The second term has expected value $1/2$, hence $h_e(X)=\frac{1}{2}\ln (2\pi e{\sigma }^2)$. Translation of the mean has no effect, while shrinking the standard deviation lowers differential entropy. A standard normal distribution has approximately $1.41894$ nats. This connects the continuous spread calculation with entropy power, whose normalization matches a Gaussian distribution's variance.

Let $X$ have a probability density function on an open interval, and let $g$ be continuously differentiable, strictly increasing there, with $g^{\prime }(x)>0$. The change of variables formula givesIf the differential entropy and the expected absolute logarithm of $g^{\prime }(X)$ are finite, taking the expectation of the negative logarithm proves the displayed formula, for a fixed base $b>1$. Local stretching reduces probability density; its contribution to differential entropy is the expected logarithmic stretch. The map is invertible, so this change does not mean that the transformation destroyed information about $X$.

The distance modulus compares received brightness with intrinsic brightness through $\mu =m-M$, the difference between apparent magnitude and absolute magnitude in the same photometric passband and magnitude zero point. With no interstellar extinction, fixed emitted spectrum and luminosity, isotropy of emission, and the local inverse-square law, it determines Euclidean distance:The ratio inside the logarithm is dimensionless. To see the factor $5$, use the inverse-square law in the magnitude difference formula:For example, $m=5$ and $M=0$ give $\mu =5$, hence $d=10\mathrm{pc}\times 10=100\mathrm{pc}$. A tenfold increase in distance adds five magnitudes; an increase of one magnitude multiplies distance by $10^{1/5}\simeq 1.584893$. The distance modulus is a logarithmic scale for distance, not a distance in length units. ^[ref].

At $10$ parsecs, $\mu =0$ and $m=M$. Within $10$ parsecs, the distance modulus is negative; for example, $\mu =-5$ gives the positive distance $d=1\mathrm{pc}$. A negative distance modulus does not imply negative distance. At cosmological distances, the inference needs luminosity distance and a K correction; the simple local formula must not silently absorb these effects.

Interstellar extinction increases apparent magnitude. The observed distance modulus then includes both geometric dimming and attenuation; extinction bias in photometric distance explains how to separate them.

For $Q(\theta \mid {\theta }_0)={\mathbb{E}}_{{\theta }_0}[\log p_{\theta }(Y,Z)\mid Y]$, the Jensen inequality givesThus any expectation-maximization algorithm M-step that increases $Q$ cannot decrease the observed-data likelihood function.^[ref]

For a short proof, assume common support and finite expressions, and keep the E-step conditional distribution $q_t(z)=p_{{\theta }^{(t)}}(z\mid y)$ fixed. The concavity of the logarithm and Jensen's inequality giveThis evidence lower bound equals $Q(\theta \mid {\theta }^{(t)})-{\mathbb{E}}_{q_t}\log q_t(Z)$. At $\theta ={\theta }^{(t)}$, the ratio inside the expectation is the constant $p_{{\theta }^{(t)}}(y)$, so equality holds. During the M-step, the second term is constant; increasing $Q$ raises a lower bound that touches the current log-likelihood. HenceIncreasing likelihood values alone do not establish convergence of the statistical parameters or attainment of a global maximum. In a differentiable finite mixture model, the gradient of $\ell$ equals the candidate-parameter gradient of $Q$ evaluated at the current fit. Thus an interior fixed point of an exact M-step is a stationary point, which need not be a local maximum; the Identical-mean EM trap is an explicit counterexample. Small changes in log-likelihood can also occur before the means have settled, so inspecting both is useful.

Entropy is an extensive quantity and a state function of an equilibrium system, measured in $\mathrm{J}{\mathrm{K}}^{-1}$. Differences between equilibrium states are calculated along a reversible thermodynamic process:Here heat entering the system is positive and $T$ is its absolute thermodynamic temperature. The reversible path used for calculation need not be the path actually taken. Dividing the actual heat by a single temperature generally fails for an irreversible process.

For example, put two rigid bodies, each with constant heat capacity $C=100\mathrm{J}{\mathrm{K}}^{-1}$, at $600\mathrm{K}$ and $300\mathrm{K}$ in an otherwise isolated system. Assume each body has a uniform internal temperature, no phase transition occurs, and no work is extracted. Their final thermal equilibrium is at $450\mathrm{K}$, because the internal energy lost by one equals that gained by the other. Using entropy from heat capacity,so $\Delta S_{\mathrm{tot}}=C\log (9/8)=11.78\mathrm{J}{\mathrm{K}}^{-1}$. One body's entropy decreases while the total increases. ^[ref]

In statistical mechanics, Boltzmann's entropy formula counts equally likely microstates compatible with a macrostate; Gibbs entropy handles a specified probability distribution over microstates. If the number of equally likely microstates doubles, entropy increases by $k_B\log 2$. The logarithm makes multiplicative counts additive for independent systems. These are precise statements about states and probabilities, not a universal measure of visible messiness. Calling entropy merely "disorder" obscures the thermal calculation above and hides which microstates are being counted. ^[ref]

The usefulness of entropy is that measurable heat capacities determine its changes, and the Second law of thermodynamics turns those changes into constraints on processes. It also connects macroscopic measurements to microscopic descriptions without requiring us to track every particle.

For the insulated gas-only free expansion of an ideal gas, the equilibrium endpoints are $(T,V_1)$ and $(T,V_2)$ with $V_2>V_1$. Because entropy is a state function, calculate their difference along a hypothetical reversible thermodynamic process: an isothermal expansion using a frictionless piston and a thermal reservoir at $T$. This comparison process receives heat and delivers work, even though the actual free expansion does neither. Its reversible heat increment is $\delta Q_{\mathrm{rev}}=pdV=nRTdV/V$, soHere $R$ is the molar gas constant, and the logarithm acts on a dimensionless ratio. The reversible reference gives the correct endpoint entropy difference because entropy is a state function; putting the actual $Q=0$ into $\Delta S=Q/T$ would misuse a relation requiring reversible heat transfer. For the actual isolated gas, all this increase is entropy production. Thus an adiabatic process can increase entropy and need not be an isentropic process. ^[ref]

In the figure's volume doubling, the ideal gas law gives $p_1=99.768\mathrm{kPa}$ and $p_2=49.884\mathrm{kPa}$, while $\Delta S=R\ln 2\simeq 5.763\mathrm{J}{\mathrm{K}}^{-1}$. The reference process receives and delivers $nRT\ln 2\simeq 1728.85\mathrm{J}$ as heat and work; its thermal reservoir loses the same entropy that the gas gains. The actual free expansion has $Q_{\mathrm{in}}=W_{\mathrm{out}}=0$ and produces that entropy internally. Reversibly compressing the gas back at $300\mathrm{K}$ would require $1728.85\mathrm{J}$ of work input and rejection of equal heat. Restoring the gas alone therefore changes the surroundings: the actual release is irreversible.

For a real gas the final temperature may differ, so the ideal-gas logarithm formula cannot simply be copied. Evaluate $S(T_2,V_2)-S(T_1,V_1)$ using that gas's equilibrium properties or a suitable reversible comparison path. If the walls and water exchange heat with it, the Second law of thermodynamics constrains the total entropy of the isolated apparatus; the gas alone is then not an isolated system.

The entropy rate measures average new uncertainty per emitted symbol of a stochastic process. Predictability from earlier symbols can make this much smaller than the information entropy of one symbol. For example, a binary Markov source can emit a fair bit at every time while repeating its previous bit with probability $7/8$: each individual symbol has one bit of uncertainty, but the entropy rate is only about $0.544$ bits per symbol.

For a finite alphabet source $X=(X_1,X_2,\dots )$, definewhen the limit exists. Base-two logarithms give bits per symbol. Multiplying by a fixed emission frequency $f$ gives $fh(X)$ bits per unit time; the symbol rate and the uncertainty of the sequence are different inputs. A strictly stationary process on a finite alphabet always has an entropy rate, equal to its conditional entropy rate.^[ref]

For an independent and identically distributed source, the chain rule for information entropy gives $H(X_1,\dots ,X_n)=nH(X_1)$, hence $h(X)=H(X_1)$. With dependence, conditioning reduces entropy and makes the past useful. The examples under Markov information source show this benefit using exact finite-block calculations.

The Euler method approximates an initial value problem $y^{\prime }=f(t,y)$, $y(t_0)=y_0$, by evaluating the derivative at the current point and moving along that tangent line for a time $h$. With $t_n=t_0+nh$, its displayed update needs one evaluation of $f$ per step. This simple rule turns local slope information into a whole approximate trajectory. MIT's course notes derive it by dropping the second-order Taylor remainder.^[ref]

Take $y^{\prime }=-2y$, $y(0)=1$, whose exact solution is the exponential function $y(t)=e^{-2t}$. The Euler method givesAt $h=0.2$, the first values are $1,0.6,0.36,0.216$. At $t=1$, five steps give $0.6^5=0.07776$, compared with $e^{-2}\approx 0.135335$. The result decays but is too small: absolute stability alone does not ensure accuracy. At $h=1.2$, the first values are $1,-1.4,1.96,-2.744$, and at $t=6$ five steps give $-5.37824$, while the exact solution is about $0.00000614421$.

For the Dahlquist test equation $y^{\prime }=\lambda y$, the Euler method has stability function $R(z)=1+z$ with $z=h\lambda$. Its absolute stability region is $|1+z|\le 1$, the closed disk centred at $-1$ with radius one. For the present real decay problem and $h>0$, bounded numerical values require $0<h\le 1$; strict decay requires $0<h<1$. At $h=1$ the values alternate $1,-1,1,\dots$ without decay. These conditions follow directly from the amplification factor $1-2h$, matching MIT's criterion for $y^{\prime }=-ay$.^[ref]

The Euler method is first order: with bounded second derivative and suitable Lipschitz continuity, its global error is $O(h)$ on a fixed interval. In this example the logarithm gives $\log (1-2h)=-2h-2h^2+O(h^3)$ for small positive $h$. At $t=nh$, this means $y_n=e^{-2t}(1-2th+O(h^2))$. It explains both the downward bias and why halving the step roughly halves the error once $h$ is sufficiently small.

The expectation-maximization algorithm fits a latent-variable model by alternating a conditional expectation over the latent variables with a maximization over the statistical parameters. Its target is the observed-data likelihood function, although its updates use the simpler complete-data log-likelihood.^[ref]

Write $y$ for the observations, $z$ for the latent variables, and $p_{\theta }(y,z)$ for their joint probability density function or probability mass function. The observed-data log-likelihood iswhere the integral becomes a sum for discrete $z$. Directly maximizing a logarithm of this sum can be awkward. The E-step instead computes a conditional distribution at the current statistical parameters ${\theta }^{(t)}$; the M-step uses it to construct a new fit. The attractive idea is to retain uncertainty about the hidden labels while recovering simple weighted means and other complete-data updates. The Known-variance Gaussian mixture EM example shows the mechanism without estimating a variance or mixture weight.

Arthur Pentland Dempster, Nan M. Laird, and Donald B. Rubin gave the general framework its name in their 1977 paper, which also discusses earlier special-case algorithms.^[ref]

An exponential polynomial is a finite sum of polynomial multiples of exponentials. For distinct exponents, its terms are linearly independent over the polynomial ring: to isolate one term, apply the product of operators $(D-{\lambda }_j{)}^{\mathrm{deg}{P}_j+1}$ for all the other terms. On the remaining polynomial each operator becomes $D+{\lambda }_0-{\lambda }_j$, which is injective because its nonzero constant preserves the leading coefficient. A nonzero exponential polynomial is an entire function of exponential type. Its maximum-modulus logarithm is $O(R+\log R)$, and Jensen's formula gives at most $O(R)$ zeros, counted with multiplicity, in a disk of radius $R$.