The following diagram summarizes conjugate prior relationships for a number of common sampling distributions.

Arrows point from a sampling distribution to its conjugate prior distribution. The symbol near the arrow indicates which parameter the prior is unknown.

*These relationships depend critically on choice of parameterization*, some of which are uncommon. This page uses the parameterizations that make the relationships simplest to state, not necessarily the most common parameterizations. See footnotes below.

**Click on a distribution** to see its parameterization. **Click on an arrow** to see posterior parameters.

See this page for more diagrams on this site including diagrams for probability and statistics, analysis, topology, and category theory.

## Parameterizations

Let *C*(*n*, *k*) denote the binomial coefficient(*n*, *k*).

The **geometric** distribution has only one parameter, *p*, and has PMF *f*(*x*) = *p* (1-*p*)* ^{x}*.

The **binomial** distribution with parameters *n* and *p* has PMF *f*(*x*) = *C*(*n*, *x*) *p ^{x}*(1-

*p*)

^{n–x}.

The **negative binomial** distribution with parameters *r* and *p* has PMF *f*(*x*) = *C*(*r* + *x* – 1, *x*) *p ^{r}*(1-

*p*)

*.*

^{x}The **Bernoulli** distribution has probability of success *p*.

The **beta** distribution has PDF *f*(*p*) = Γ(α + β) *p*^{α-1}(1-*p*)^{β-1} / (Γ(α) Γ(β)).

The **exponential** distribution parameterized in terms of the rate λ has PDF *f*(*x*) = λ exp(-λ *x*).

The **gamma** distribution parameterized in terms of the rate has PDF *f*(*x*) = β^{α} *x*^{α-1}exp(-β *x*) / Γ(α).

The **Poisson** distribution has one parameter λ and PMF *f*(*x*) = exp(-λ) λ* ^{x}*/

*x*!.

The **normal** distribution parameterized in terms of precision τ (τ = 1/σ^{2})

has PDF *f*(*x*) = (τ/2π)^{1/2} exp( -τ(*x* – μ)^{2}/2 ).

The **lognormal** distribution parameterized in terms of precision τ has PDF *f*(*x*) = (τ/2π)^{1/2} exp( -τ(log(*x*) – μ)^{2}/2 ) / *x*.

## Posterior parameters

For each sampling distribution, assume we have data *x*_{1}, *x*_{2}, …, *x _{n}*.

If the sampling distribution for *x* is **binomial**(*m*, *p*) with *m* known, and the prior distribution is **beta**(α, β), the posterior distribution for *p* is **beta**(α + Σ*x _{i}*, β +

*mn*– Σ

*x*). The

_{i}**Bernoulli**is the special case of the binomial with

*m*= 1.

If the sampling distribution for *x* is **negative binomial**(r, *p*) with *r* known, and the prior distribution is **beta**(α, β), the posterior distribution for *p* is **beta**(α + *nr*, β + Σ*x _{i}*). The

**geometric**is the special case of the negative binomial with

*r*= 1.

If the sampling distribution for *x* is **gamma**(α, β) with α known, and the prior distribution on β is gamma(α_{0}, β_{0}), the posterior distribution for β is **gamma**(α_{0} + *nα*, β_{0} + Σ*x _{i}*). The

**exponential**is a special case of the gamma with α = 1.

If the sampling distribution for *x* is **Poisson**(λ), and the prior distribution on λ is **gamma**(α_{0}, β_{0}), the posterior on λ is **gamma**(α_{0} + Σx_{i}, β_{0} + *n*).

If the sampling distribution for x is **normal**(μ, τ) with τ known, and the prior distribution on μ is **normal**(μ_{0}, τ_{0}), the posterior distribution on μ is **normal**((μ_{0} τ_{0} + τ Σ*x _{i}*)/(τ

_{0}+

*n*τ), τ

_{0}+

*n*τ).

If the sampling distribution for x is **normal**(μ, τ) with μ known, and the prior distribution on τ is **gamma**(α, β), the posterior distribution on τ is **gamma**(α + *n*/2, (*n*-1)*S*^{2}) where *S*^{2} is the sample variance.

If the sampling distribution for x is **lognormal**(μ, τ) with τ known, and the prior distribution on μ is **normal**(μ_{0}, τ_{0}), the posterior distribution on μ is **normal**((μ_{0} τ_{0} + τ Π*x _{i}*)/(τ

_{0}+

*n*τ), τ

_{0}+

*n*τ).

If the sampling distribution for *x* is **lognormal**(μ, τ) with μ known, and the prior distribution on τ is **gamma**(α, β), the posterior distribution on τ is **gamma**(α + *n*/2, (*n*-1)*S*^{2}) where *S*^{2} is the sample variance.

## References

A compendium of conjugate priors by Daniel Fink.

See also Wikipedia’s article on conjugate priors.

## Working together

Call or email me to discuss how I may be able to help you with Bayesian statistics.