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Translate the inequality inside the cumulative distribution function:Differentiating with the chain rule gives the probability density functionalmost everywhere. Translation shifts the probability distribution without changing its shape or its total probability.

For any real $y$, put $k=\lfloor y\rfloor$. Since $Y$ is integer-valued,Continuity of the cumulative distribution function of $X$ therefore givesThe strict inequality $X<k+1$ matters before using the absence of point masses. The resulting probability distribution is discrete, with probability mass functionThus $Y$ has no ordinary probability density function with respect to length on the real line. Its cumulative distribution function is a step function; its derivative is zero almost everywhere and does not recover its probabilities. The floor function has converted a density into masses on the integers.

Conditioning on $Z=z$ has positive probability exactly when $z=2r+1$ for an integer $r\ge 0$. This is the same event as $X+Y=r$. The addition of independent Poisson random variables gives a Poisson distribution with parameter $\lambda +\mu$ for the sum. Therefore, for $0\le t\le r$,Thus $X\mid \{Z=2r+1\}\sim \mathrm{Binomial}(r,\lambda /(\lambda +\mu ))$. The case $r=0$ is a constant zero random variable. For even, nonpositive, or noninteger $z$, the conditioning event has zero probability, so an event-conditional probability distribution is not specified by the quotient formula. This is a two-component case of Poisson-multinomial conditioning.

Using the rate parameter convention for the exponential distribution, independence gives the joint probability density functionFor $z\ge 0$, the survival probability of the minimum isThus $Z$ has exponential distribution with rate $3$, andValues of a probability density function at the single endpoint $0$ do not affect the probability distribution.

Normalization of the probability density function requires $1=c{\int }_1^{\infty }{x}^{-t}dx=c/(t-1)$. Hence $c=t-1$. It is positive for the given $t>2$, and the density is nonnegative, so this does define a probability distribution. It is a Pareto distribution with scale $1$ and shape $t-1$.

Percolation theory asks how many local connections must be available before a large connected structure appears. Start with a graph, randomly retain some edges or vertices, and study the resulting percolation clusters. In independent bond percolation, every edge is open with probability $p$, independently of the others. In independent site percolation, the random decisions concern vertices instead. A percolation cluster is a connected component of a graph formed by the available connections; an open path joins two vertices exactly when they belong to the same percolation cluster.

The attraction is that a local rule can produce a global change. One open edge says little about travel across a large graph. Many alternative graph paths can nevertheless make distant connections likely. The model separates the geometry of possible connections from the probability distribution of which connections are usable. This was the central perspective of Broadbent and Hammersley's 1957 paper, which discussed porous solids and disease transmission as interpretations of the same mathematical structure: ^[ref]. Independent bond percolation supplies a simplified model of those systems; the physical interpretation still requires justification of its independence assumptions.

For a completely calculable example, consider the four edges around a square and ask whether opposite corners $A$ and $C$ are connected. There are two graph paths, each using two edges. Each open path has probability $p^2$. Their events are independent events because the paths share no edges, even though they share endpoints. The inclusion-exclusion principle therefore givesAt $p=1/2$, the connection probability is $7/16$, larger than the $1/4$ for either chosen graph path. Counting all sixteen equally likely edge configurations gives the same answer: two configurations with exactly two open edges, four with three, and one with four connect $A$ to $C$. Connectivity benefits from alternative routes; adding their probabilities without subtracting their overlap would count the all-open configuration twice.

This finite calculation is already useful for studying redundant connections. The deeper question is what happens as the graph becomes arbitrarily large: does connectivity remain confined to finite percolation clusters, or can an infinite percolation cluster appear? The percolation probability and percolation critical probability make that question precise. A finite picture can illustrate the geometry, but cannot establish the existence of an infinite percolation cluster.

An encryption scheme has perfect secrecy when the ciphertext and plaintext are independent random variables, so observing the ciphertext does not change the plaintext's probability distribution.

Photon statistics is the probability distribution of the number operator in specified field modes, or the distribution of registered counts in a specified gate. Those distributions agree only for an appropriate ideal measurement. Mode selection, losses, and detector dead time matter. For a one-mode density matrix, $p_n=⟨n|\rho |n⟩$ andThese two sums already distinguish preparations with the same expected value.

The Poisson-Dirichlet family consists of laws of decreasing random mass partitions $(p_i)$ with ${\sum }_i{p}_i=1$. Its two-parameter notation is $PD(\alpha ,\theta )$, with $0\le \alpha <1$ and $\theta >-\alpha$. The conventional one-parameter probability distribution $PD(\theta )$ means $PD(0,\theta )$, for $\theta >0$, and can be constructed from normalized gamma jumps.

A Homogeneous Poisson process models arrivals at a constant rate $\lambda >0$. Its counting process $N_t=N(t)$ counts arrivals in $(0,t]$, with $N_0=0$. Its paths are right-continuous nondecreasing functions, take nonnegative integer values, and have unit jumps almost surely. The defining Poisson distribution of each increment isIt has stationary increments: the probability distribution above depends on the duration $t$, not the starting time $s$. It also has independent increments: counts in any finite collection of disjoint time intervals are independent random variables. Together these conditions specify the joint count laws, not merely the Poisson distribution at a single time.^[ref]

The rate has units of arrivals per unit time; $\lambda t$ is dimensionless. The Poisson distribution givesFor a toy rate $\lambda =2$ per minute and a duration of $1.5$ minutes, the expected value and variance of the count are both $3$. The probability of no arrival is $e^{-3}\approx 0.049787$, and the probability of exactly three is $3^3{e}^{-3}/3!=\frac{9}{2}{e}^{-3}\approx 0.224042$. In two consecutive one-minute intervals, the probability of exactly two arrivals in the first and exactly one in the second is $(2e^{-2})(2e^{-2})=4e^{-4}\approx 0.073263$, using independent increments. These are exact model calculations with rounded decimals, not measured frequencies.

The diagram's nine hand-chosen arrivals illustrate a path; the line $\lambda t$ is an expected value, not a prescribed trajectory. Constant rate does not imply evenly spaced arrivals. Poisson interarrival times connect the count description to waiting, while Poisson thinning explains how independently routing arrivals produces separate streams. This makes the Poisson process useful as a simple arrival model for a queue, provided constant rate and independent increments are plausible. For a deterministic changing rate, use an Inhomogeneous Poisson process; bunching or history-dependent arrivals need additional assumptions. Conditional on $N_t=n$, the arrival times are the order statistics of $n$ independent uniform distributions on $[0,t]$, as developed in Poisson process conditional arrival times.

Polymerization converts a monomer, or a mixture of monomers, into a polymer: repeated chemical reactions establish a connected molecular chain or network. The central question is how connections form, not merely whether a material becomes solid. Joining small molecules can produce a thread that can be handled, while the individual molecules remain invisible. See IUPAC, Basic Definitions of Terms Relating to Polymers, definition 2.2.

In step-growth polymerization, complementary reactive groups on monomers and on shorter or longer chains can react with one another. A two-unit chain can join a three-unit chain to make a five-unit chain; growth does not require a specially active end to add only free monomers. An example is the Nylon 6,10 reaction of ${\mathrm{H}}_2\mathrm{N}(\mathrm{C}{\mathrm{H}}_2{)}_6\mathrm{N}{\mathrm{H}}_2$ with $\mathrm{ClC}(=\mathrm{O})(\mathrm{C}{\mathrm{H}}_2{)}_8\mathrm{C}(=\mathrm{O})\mathrm{Cl}$: each amine end can form an amide connection with an acid-chloride end, with formal elimination of $\mathrm{HCl}$. Both reactants have two suitable ends, allowing repeated joining. See Paula Hammond, MIT 10.569 lecture 2: step-growth joining and Hammond, lecture 5, p. 4: acid chloride–amine reaction.

In chain-growth polymerization, a monomer reacts with an active site on a growing chain, and the growth step regenerates an active site. Schematically, $P_j^{\ast }+M⟶P_{j+1}^{\ast }$, where $j$ counts incorporated monomeric units and the star denotes reactivity rather than a particular electric charge. Ethene forming polyethylene is an example: the double chemical bond is converted into single chemical bonds as the carbon backbone grows. Initiation creates the active chains; subsequent reactions can deactivate them or transfer reactivity to another molecule. See IUPAC: chain polymerization and John McMurry, Organic Chemistry, 31.1: chain growth. Whether growth is stepwise or carried by an active site is a different question from whether small byproducts are released: chain-growth polymerization can also release them.

A motivating experiment: drawing nylon from a liquid interface. Feldwinn's UCSB demonstration places $25\mathrm{mL}$ of an aqueous chemical solution of hexamethylenediamine, ${\mathrm{H}}_2\mathrm{N}(\mathrm{C}{\mathrm{H}}_2{)}_6\mathrm{N}{\mathrm{H}}_2$, in a $100\mathrm{mL}$ beaker, then gently adds $25\mathrm{mL}$ of sebacoyl chloride, $\mathrm{ClC}(=\mathrm{O})(\mathrm{C}{\mathrm{H}}_2{)}_8\mathrm{C}(=\mathrm{O})\mathrm{Cl}$, in an organic solvent. The procedure specifies hexane; other parts of that handout list cyclohexane. The layers remain separate. The instructor catches material at their interface with tweezers and winds it slowly onto a glass rod; the handout reports continued spooling of Nylon 6,10. It specifies freshly prepared acid-chloride chemical solution and washing the thread before touching it. These are observations and conditions of a supervised laboratory demonstration, not measurements of chain lengths. See Feldwinn, UCSB Demonstration Library, Demo 044, pp. 1–3.

The mechanistic interpretation is that the monomers encounter one another near the interface, where amide connections produce the film; withdrawing it exposes a region where more material can form. An authored research demonstration independently reports a rapidly formed interfacial film collapsing into a thread on withdrawal, using cyclohexane and an aqueous chemical solution containing potassium hydroxide. See Polymer Day: Outreach Experiments for High School Students, Journal of Chemical Education (2017), nylon demonstration. This is the striking observation: a thread much longer than the beaker can emerge because material is continually supplied to the formation region, rather than because a pre-existing thread was hidden in the liquid.

Useful proposed controls, not results reported in those demonstrations, omit one monomer at a time while retaining the same solvents, liquid amounts, and pulling procedure. The prediction is loss of a renewable drawable Nylon 6,10 film; merely finding cloudiness would not establish polymerization, since other chemical reactions, including acid-chloride hydrolysis, can occur. See McMurry, 21.4: competing acid-chloride hydrolysis. Stopping and restarting withdrawal would probe renewal at the interface. Even successful drawing does not measure the degree of polymerization, establish a chain-length probability distribution, or prove the entire growth mechanism. Chemical identification and a molecular-size measurement would be needed for those stronger conclusions. These controls and limits are inferences from the setup, not invented experimental data.

Why almost complete reaction matters in step growth. For ideal linear step-growth polymerization, assume exactly two reactive groups per starting molecule, equal numbers of complementary groups, only joining of different chains, no rings, no branching, and no loss of chain material. Let $N_0$ count starting monomer molecules, including both reactant species, and let $p$ be the fraction of either type of reactive group consumed. There are initially $N_0$ groups of each type, so $p{N}_0$ connections reduce the number of chain-bearing molecules to $N=N_0(1-p)$; the count excludes small byproducts but includes unreacted monomers. The number-average degree of polymerization, measured in monomeric units, is thereforeThus $p=0.90$, $0.99$, and $0.999$ give $X_n=10$, $100$, and $1000$. Losing the last fraction of unreacted groups matters enormously. See Hammond, lecture 3: balanced step-growth conversion. This counts residues of both feed species; a Nylon 6,10 interior constitutional repeating unit contains two such monomeric units.

For an $AA+BB$ feed with $A_0\le B_0$ initial reactive-group counts, put $r=A_0/B_0$ and now let $p$ denote the fraction of the limiting $A$ groups consumed. Under the same linear, no-ring assumptions, $N_0=(A_0+B_0)/2$ and $N=N_0-p{A}_0$, givingFor $r=0.98$, even complete consumption of $A$ gives $X_n=(1.98)/(0.02)=99$. Stoichiometry controls chain length as well as yield. See Hammond, lecture 2, pp. 2–3: stoichiometric imbalance. The liquid-interface demonstration is not a homogeneous, balanced reactor, so its thread length cannot be inserted into this formula as a molecular chain length.

A contrasting chain count. In ideal living polymerization, assume no chain termination or irreversible chain transfer, all chains initiated before the counted growth, one chain per effective initiator, a fixed chain count $C$, and every consumed monomer molecule incorporated into those chains. If $M_0$ is the initial number of feed monomer molecules and $q$ their incorporated fraction, conservation gives $X_n=q{M}_0/C=qR$ in monomeric units, excluding initiator fragments. For $R=200$, incorporating half the feed gives $X_n=100$. If initiation consumes one monomer molecule per chain, the fixed-chain interval starts at $q=1/R$, rather than $q=0$. See Hammond, lecture 21, p. 1: ideal living-chain count. The mean does not imply identical chain lengths. Living polymerization does not require reversible loss of monomers; the defining absence of chain termination and irreversible chain transfer is explained in Devon Shipp, IUPAC Polymer Education workshop, Radical Polymerizations II, slide 50. Ordinary chain-growth polymerization with new chains, termination, or transfer need not follow the straight line.

The useful connection is between local chemistry and a macroscopic material: selecting reactive ends and their proportions controls connectivity, while the resulting polymer's conformation is a separate problem described by models such as the freely jointed chain or Gaussian chain. Formation of chemical bonds, the count of monomeric units, and the count of statistical segments must be kept distinct.

The expected value of a numerical characteristic under the target population's probability distribution, when it exists. A sample mean estimates this fixed statistical parameter from observed data; it is not itself the population value.

The population risk of a prediction rule is its expected loss on a fresh observation from the target probability distribution.

Split the units into visible units $v$, whose patterns occur in the training data, and hidden units $z$. Let $q(v)$ be the empirical probability distribution and maximize the average log-likelihood $\ell ={\sum }_{v}q(v)\log p_{\beta }(v)$, where $p_{\beta }(v)={\sum }_z{p}_{\beta }(v,z)$. The positive phase clamps $v$ to data and averages over $q(v)p_{\beta }(z\mid v)$; the negative phase releases all units and averages over $p_{\beta }(v,z)$. The displayed difference uses these two expectations. When both units are visible, the positive pair product is supplied directly by the data.

For a proof, write $\log p_{\beta }(v)=\log {\sum }_z{e}^{-\beta E(v,z)}-\log Z$. Differentiating each finite sum gives, respectively, $-\beta ⟨\partial E{⟩}_{z\mid v}$ and $-\beta ⟨\partial E{⟩}_{v,z}$. Since $\partial E/\partial w_{ij}=-x_i{x}_j$, subtraction proves the rule. Similarly ${\partial }_{{\theta }_i}\ell =-\beta (⟨x_i{⟩}_{+}-⟨x_i{⟩}_{-})$. A small positive step along these gradients increases log-likelihood. The negative term suppresses associations overproduced by the model; strengthening observed pairs alone misses this correction. The identity requires equilibrium expectations; estimates from insufficiently mixed sampling can give a biased update.

Potential theory studies harmonic functions, subharmonic functions, and the Laplace operator, including harmonic measure, regular boundary points, and the Dirichlet problem. The Brownian representation of the Dirichlet problem connects these analytic objects with exit probability distributions.

Probabilistic number theory studies arithmetic functions and integer sets through probability distributions, concentration, and limit theorems.

Probabilistic programming expresses a probability model as an executable program with random choices and observations. Executing the generative part induces a probability distribution; observations then restrict or weight executions, and inference computes the resulting conditional distribution. Program syntax and inference algorithm are separate choices: the same model can be evaluated by rejection sampling, importance sampling or Markov chain Monte Carlo.

On a probability space, the same inequality readsIf the displayed moments are finite, the product is an integrable random variable. The random variables can be dependent: the estimate controls their possible overlap without assuming a factorization of the expected value. This is useful when a calculation needs the product to be integrable but the joint probability distribution is complicated.

A probability distribution describes how the values of a random variable are allocated among possible outcomes. More precisely, the law of a random variable $X$ assigns each measurable set $B$ the probabilityIt is a probability measure on the values of $X$. The underlying experiment can be more elaborate than the output: different experiments may produce the same probability distribution. The law of a random variable retains the probabilities needed to answer questions about that output. ^[ref].

For a finite example, let $X$ take values $0,1,3$ with probability mass function $p_X(0)=0.2$, $p_X(1)=0.5$, $p_X(3)=0.3$. Its probabilities sum to one. The event $0<X\le 1$ has probability $0.5$, whereas $0\le X\le 1$ has probability $0.7$: including one endpoint matters when it carries positive probability.

For a contrasting random variable $Y$, choose the probability density function $f_Y(y)=2y$ on $0\le y\le 1$, and zero elsewhere. It integrates to one. Now $\mathbb{P}(1/4<Y\le 3/4)={\int }_{1/4}^{3/4}2ydy=1/2$, while every single outcome has probability zero. The probability density function can have height $f_Y(0.8)=1.6$: probabilities come from integrals over outcome ranges.

The cumulative distribution function answers all threshold questions for a real random variable, regardless of whether there are jumps or a probability density function. A quantile function reverses the direction of the question: given a cumulative probability level, find a threshold. This connects descriptive summaries to inverse transform sampling, which constructs a chosen probability distribution from a uniform distribution.

For a nonnegative integer-valued random variable $X$, write $p_k=\mathbb{P}(X=k)$. Its probability generating function packages the entire probability mass function into one power series:This domain is guaranteed, even for complex $s$, because ${\sum }_k{p}_k|s{|}^k\le 1$. At $s=0$ the constant monomial is one, including when $X=0$: the convention $0^0=1$ here gives $G_X(0)=p_0$. Also $G_X(1)=1$. The power series may converge beyond this domain, but that needs an additional argument. Encoding a probability distribution this way turns convolution of independent random variables into multiplication.^[ref]