Buffon's noodlewikipedia

In geometric probability, the problem of Buffon's noodle is a variation on the well-known problem of Buffon's needle, named after Georges-Louis Leclerc, Comte de Buffon who lived in the 18th century.

### Buffon's needle

**his needlehis needle problem**

In geometric probability, the problem of Buffon's noodle is a variation on the well-known problem of Buffon's needle, named after Georges-Louis Leclerc, Comte de Buffon who lived in the 18th century.

A particularly nice argument for this result can alternatively be given using "Buffon's noodle".

### Barbier's theorem

This implies Barbier's theorem asserting that the perimeter is the same as that of a circle.

An elementary probabilistic proof of the theorem can be found at Buffon's noodle.

### Crofton formula

**classic theorem of CroftonCauchy-Crofton theorem**

### Geometric probability

**geometric probabilityGeometrical Probability**

In geometric probability, the problem of Buffon's noodle is a variation on the well-known problem of Buffon's needle, named after Georges-Louis Leclerc, Comte de Buffon who lived in the 18th century.

### Georges-Louis Leclerc, Comte de Buffon

**BuffonComte de BuffonLeclerc de Buffon**

### Plane curve

**plane curvecomplex plane curvecurve**

The interesting thing about the formula is that it stays the same even when you bend the needle in any way you want (subject to the constraint that it must lie in a plane), making it a "noodle"—a rigid plane curve.

### Probability distribution

**probability distributiondistributioncontinuous probability distribution**

The probability distribution of the number of crossings depends on the shape of the noodle, but the expected number of crossings does not; it depends only on the length L of the noodle and the distance D between the parallel lines (observe that a curved noodle may cross a single line multiple times).

### Expected value

**expected valueexpectationexpected**

The probability distribution of the number of crossings depends on the shape of the noodle, but the expected number of crossings does not; it depends only on the length L of the noodle and the distance D between the parallel lines (observe that a curved noodle may cross a single line multiple times). These random variables are not independent, but the expectations are still additive due to the linearity of expectation:

### Polygonal chain

**polygonal chainpolylinePolygonal curve**

First suppose the noodle is piecewise linear, i.e. consists of n straight pieces.

### Independence (probability theory)

**independentstatistically independentindependence**

These random variables are not independent, but the expectations are still additive due to the linearity of expectation:

### Curve of constant width

**curve of constant widthcurves of constant widthconstant diameter**

In case the noodle is any closed curve of constant width D the number of crossings is also exactly 2.