ProbabilidadMarkov Chains

Absorption and First-Step Analysis

Condition on the first step to get equations for the chance of absorption and the expected time to get there, as in the gambler's ruin.

Sin empezar Ir a la práctica

Esta lección aún no está traducida. Por ahora se muestra en inglés; la versión en español está en camino.

Some states of a Markov chain are traps: once entered, the chain stays forever. A gambler who reaches \$0 or a target stops playing. Two questions follow: where does the chain end up, and how long does it take? Conditioning on the first step turns each question into a small linear system, solved as in elimination.

Idea clave

A state $j$ is absorbing if $P_{jj} = 1$. Let $h_i$ be the chance of ending in a chosen absorbing state starting from $i$, and $m_i$ the expected number of steps to absorption. Then $h = 1$ in the chosen state, $h = 0$ in the other absorbing states, $m = 0$ in all absorbing states, and for every other state $$h_i = \sum_j P_{ij}h_j, \qquad m_i = 1 + \sum_j P_{ij}m_j.$$

Por qué funciona

Condition on the first step. From $i$ the chain moves to $j$ with probability $P_{ij}$, and by the Markov property it then behaves as if it had started at $j$. So the chance of ending in the target is the weighted average $\sum_j P_{ij}h_j$ (total probability). For the time, the step just taken counts 1, and then the expected remaining time from $j$ is $m_j$: $m_i = 1 + \sum_j P_{ij}m_j$. Absorbing states supply the boundary values.
Gambler's ruin with fair bets between 0 and 4 dollars. The equations from the first step are written out and solved.

Worked examples

Ejemplo resuelto: Gambler's ruin

A gambler with \$$k$ bets \$1 at a time on fair coin flips until reaching \$$N$ or \$0. Find the chance of reaching \$$N$.
  1. $h_0 = 0$, $h_N = 1$, and for $0 < i < N$: $h_i = \frac{1}{2}h_{i - 1} + \frac{1}{2}h_{i + 1}$.
  2. Rearranged: $h_{i + 1} - h_i = h_i - h_{i - 1}$. The steps are equal, so the $h_i$ rise in a straight line from 0 to 1.
  3. $h_k = \frac{k}{N}$; with $k = 1$ and $N = 4$, the chance is $\frac{1}{4}$.

Respuesta: $\frac{k}{N}$

Ejemplo resuelto: How long it lasts

For the same game, find the expected number of bets.
  1. $m_0 = m_N = 0$ and $m_i = 1 + \frac{1}{2}m_{i - 1} + \frac{1}{2}m_{i + 1}$.
  2. Try $m_i = i(N - i)$: $$1 + \frac{1}{2}(i - 1)(N - i + 1) + \frac{1}{2}(i + 1)(N - i - 1) = i(N - i)$$ It fits the equations and the boundary values.
  3. From \$1 with $N = 4$: $1 \cdot 3 = 3$ bets on average. From \$2: $2 \cdot 2 = 4$.

Respuesta: $k(N - k)$

Ejemplo resuelto: Waiting for two heads in a row

Flip a fair coin until HH appears. Find the expected number of flips.
  1. States: "start" and "just flipped H". Let $m_0, m_1$ be the expected flips still needed.
  2. $m_0 = 1 + \frac{1}{2}m_1 + \frac{1}{2}m_0$ and $m_1 = 1 + \frac{1}{2} \cdot 0 + \frac{1}{2}m_0$ (a T sends you back to the start).
  3. The first gives $m_0 = 2 + m_1$; then $m_1 = 1 + \frac{1}{2}(2 + m_1)$, so $m_1 = 4$ and $m_0 = 6$.

Respuesta: 6 flips

With an unfair coin, winning with probability $p$, the steps $h_{i + 1} - h_i$ grow by the factor $r = \frac{1 - p}{p}$ each time, and $h_k = \frac{1 - r^{k}}{1 - r^{N}}$ (for $p \ne \frac{1}{2}$).

Error común

Forgetting to add 1 for the step just taken when writing the equations for expected times. Without it, $m_i = \sum P_{ij}m_j$ is solved by $m = 0$ everywhere, which is plainly wrong. Check: absorption probabilities have no "1 +" and lie between 0 and 1; expected times have "1 +" in every equation of a non-absorbing state.

Práctica

Apruebas con 3 respuestas correctas, las 2 últimas seguidas. Cada respuesta correcta da 3 XP.

Cargando la práctica…

Se apoya en

Lleva a

Este es uno de los peldaños más altos de Probabilidad.

Fuentes y lecturas

  • Grinstead and Snell, Introduction to Probability (2nd revised edition, American Mathematical Society; free from Dartmouth College), chapter 11 (Markov Chains), section 11.2, absorbing Markov chains; chapter 12 (Random Walks), section 12.2, gambler's ruin
  • MIT OpenCourseWare 18.440, Probability and Random Variables (Spring 2014), lecture notes on Markov chains and first-step analysis