Fun With Random Walks

Kevin Feasel



Emrah Mete simulates a random walk in R:

Let’s consider a game where a gambler is likely to win $1 with a probability of p and lose $1 with a probability of 1-p.

Now, let’s consider a game where a gambler is likely to win $1 and lose $1 with a probability of 1. The player starts the game with X dollars in hand. The player plays the game until the money in his hand reaches N (N> X) or he has no money left. What is the probability that the player will reach the target value? (We know that the player will not leave the game until he reaches the N value he wants to win.)

The problem of the story above is known in literature as Gambler’s Ruin or Random Walk. In this article, I will simulate this problem with R with different settings and examine how the game results change with different settings.

Click through for the script and analysis.  There’s a reason they call this game the gambler’s ruin.

Related Posts

Inline Operators In R With wrapr

John Mount shows how to use inline operators in R with the wrapr package: The above code is assuming you have the wrapr package attached via already having run library('wrapr'). Notice we picked R-related operator names. We stayed away from overloading the + operator, as the arithmetic operators are somewhat special in how they dispatch in R. The goal wasn’t […]

Read More

Feature And Text Classification Using Naive Bayes In R

I wrap up my series on the Naive Bayes class of algorithms, finally writing some code along the way: Now we’re going to look at movie reviews and predict whether a movie review is a positive or a negative review based on its words. If you want to play along at home, grab the data set, […]

Read More


February 2018
« Jan Mar »