← Back to article

Equation 14 · Einstein's Random Walk and the Mathematics of Genetic Drift

What does this equation mean?

μ(x,t)\mu(x,t)

Read the formula alongside the article passage below. Each part has a deeper page with its role in the equation, the supporting passage and nearby citations.

This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.

Read it piece by piece

μ\mu

Symbol mu

mu is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

Understand this part →

xx

Symbol x

x is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

Understand this part →

tt

Symbol t

t is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

Understand this part →

How to interpret it

Read this expression with the definitions, units, and assumptions supplied by the article.

What the article says around this equation

where the first right-hand term carries the probability density along a deterministic drift μ(x,t)\mu(x,t) and the second spreads it by diffusion at rate D(x,t) [ 3 ] . Andrei Kolmogorov arrived at the identical equation independently in 1931, from pure probability theory, as the forward equation governing how the probability distribution of any continuous-time Markov process evolves [ 3 ] . Kolmogorov’s work also produced a companion equation, run backward in time from a fixed outcome rather than forward from a fixed start, that turns out to be the more convenient tool for asking what a process is heading toward rather than where it began — a distinction that mattered when the same mathematics…
Read the full surrounding passage
where the first right-hand term carries the probability density along a deterministic drift μ(x,t)\mu(x,t) and the second spreads it by diffusion at rate D(x,t) [ 3 ] . Andrei Kolmogorov arrived at the identical equation independently in 1931, from pure probability theory, as the forward equation governing how the probability distribution of any continuous-time Markov process evolves [ 3 ] . Kolmogorov’s work also produced a companion equation, run backward in time from a fixed outcome rather than forward from a fixed start, that turns out to be the more convenient tool for asking what a process is heading toward rather than where it began — a distinction that mattered when the same mathematics was pointed at a population instead of a particle. That third, unrelated derivation is what turned Einstein’s equation from a fact about pollen grains into a piece of mathematics indifferent to what its variable represents — a state of affairs population genetics was about to test.

Read the equation in its article →

Sources cited in the surrounding passage

These citations give research context. Read each source to check which claims it supports.

Return to Einstein's Random Walk and the Mathematics of Genetic Drift

See this formula across 1 published context →

Browse the mathematical compendium →