Today I Learned

Some of the things I've learned every day since Oct 10, 2016

Category Archives: probability

91: Categorical Distribution (Probability)

categorical distribution is one which describes a random variable which can take k possible values, the probability of each possible outcome being known. For instance, the Bernoulli distribution is a special case of the categorical distribution, where k = 2 (one outcome being ‘success’, the other ‘failure’). The distribution of the outcomes of rolling a (not necessarily fair) 6-sided die would also be an example, with k = 6.

90: The Standard (Unit) Simplex

The standard n-simplex, sometimes denoted \Delta^n, is the n-simplex in \mathbb{R}^{n + 1} with its vertices at the n + 1 ‘unit’ points

e_0 = (1, 0, 0, \dots, 0)

e_1 = (0, 1, 0, \dots, 0)

e_2 = (0, 0, 1, \dots, 0)


e_n = (0, 0, 0, \dots, 1).

For example, \Delta ^0 is the point 1 in \mathbb{R}^1\Delta ^1 is the line segment from (0, 1) to (1, 0) in \mathbb{R}^2, and so on. A formal description is

\Delta ^n = \{(x_0, x_1, \dots, x_n) \in \mathbb{R}^{n+1} | \sum _{i = 0} ^{n} x_i = 1, x_i \geq 0 \}.

One possible interpretation of \Delta ^n is as the set of possible probability distributions of a categorical distribution on n+1 variables.

84: Some Properties of Transition Matrices (Probability)

The following are some miscellaneous related properties of transition matrices describing stochastic processes such as Markov chains.

A ‘transition matrix’ is a square matrix A with real entries in [0, 1] such that the sum of the entries of any given of A is equal to 1, the interpretation being that A_{i, j} is the probability of a stochastic process transitioning from state j to state i. A ‘regular’ matrix is a square matrix A such that A^n contains strictly positive entries for some n > 1.

For any transition matrix:

  1. Every transition matrix has 1 as an eigenvalue.

For regular transition matrices:

  1. The algebraic multiplicity of 1 as an eigenvalue of A is 1.
  2. The columns of \lim _{n \rightarrow \infty} A^n are identical, all being the unique A-invariant probability vector v such that the sum of the entries of v is 1 (because it’s a probability vector) and A v = v.
  3. With v defined as above, for any probability vector w\lim _{n \rightarrow \infty} A^n w = v. That is, the system or process described by A eventually converges to a distribution described by v, regardless of it’s initial state.

66: Regular Transition Matrices

The transition matrix P of a Markov chain X is called regular if some positive power of the matrix P^n, n \geq 1 contains only positive entries.

60: Closure of MIN on Exponential Random Variables

The minimum of random variables E_1, E_2, ... , E_n with corresponding parameters \lambda _1, \lambda _2, ... , \lambda _n is itself a random variable with parameter \lambda = \sum _{i = 1} ^n \lambda _i.

It is not, however, the case that the maximum is also exponentially distributed.

52: Ergodicity in Markov Chains

A state s in a Markov chain X is ergodic iff it is aperiodic and recurrent. If all states in a Markov chain are ergodic, then the chain itself is said to be ergodic. This is equivalent to the condition that there is a number n such that any state in X can be reached by any other state in exactly n steps.

47: Invariant Distributions as Eigenvectors

Since a stationary distribution \pi of a finite Markov chain X satisfies \pi P = \pi, where P is the transition matrix of X, it can be seen as an eigenvector of eigenvalue \lambda = 1 under the linear transformation by P. Specifically, \pi is the intersection of the eigenspace E_1 with the hyperplane formed by the constraint that \sum _{i = 1} ^n \pi (i) = 1.

(Here the vector space in question is \mathbb{R}^n, where n is the number of states in X.)

46: Absorbing States of Markov Chains

A state S in a Markov chain X is absorbing iff it is impossible for the chain to leave S once it’s there. If an absorbing state is accessible from any arbitrary state in X, then X is likewise said to be absorbing.

45: Transient and Recurrent States of Markov Chains

A state S of a Markov chain X is transient if, given we start from S, there is a non-zero probability that we will never return to S. A state is transient iff the expected number of visits to that state is finite.

Conversely, S is recurrent if, given we start from S, the probability we will eventually return to S is 1. A state is recurrent iff the expected number of visits to that state is infinite.

43: Aperiodicity of Markov Chains

A state s in a Markov chain X is aperiodic iff there exists an n such that

P(X_{n'} = s | X_0 = s) > 0 \quad \forall n' \geq n.

That is, a state is aperiodic if the chain can always loop back to this state in an arbitrary number of steps.

A Markov chain is aperiodic iff every state in the chain is aperiodic. If the chain is irreducible, then the existence of a single aperiodic state implies that the entire chain is aperiodic.