I'm very glad if you use or at least read sometimes my blog. However, I'd like to give you a list of wonderful blogs which are useful if you are interested in applied mathematics and programming for simulation.
My favorite one is written by Arthur Charpentier, a French mathematician. His blog is called Freakonometrics and is a wonderful store of R codes and explanations of statistics:
http://freakonometrics.blog.free.fr/
R websites:
-I can't do a reasonable list of websites without talking about R-cran the website of R. http://cran.r-project.org/. You can find nearly everything about R on this website, especially an important documentation about the software.
-R-bloggers : http://www.r-bloggers.com/. This blog is a useful summary of many blogs related to R. If you are interested in R in general, or if you want to learn how to code in R or if you are interested in a particular mathematical issue, this website is a great hub of knowledge.
-R-statistics (http://www.r-statistics.com/) is another valuable resource for examples of codes in R.
-SAS and R (http://sas-and-r.blogspot.co.uk/). Even though this website is not only in R but also in SAS, I love this website. It made my life better when I was coding in SAS or in R.
-Stackoverflow (http://stackoverflow.com/). This website is about programming in general. It is useful when you have any bug you can't solve. It's a forum where people help or ask for help about any programming problems. When you can't already find someone who had experienced the same problem than you, you can ask for help. The answers are very reactive and you don't have to wait too long before being helped.
Mathematics websites:
-A good blog about probability and its applications is http://probabilityandstats.wordpress.com/. I like it because funny topics are analyzed with a serious approach.
-Gower's weblog is certainly the most well known blog about mathematics. Therefore it has to be in my list. http://gowers.wordpress.com/
Mar
4
Pricing of a financial product : A pricer of a call option.
The financial market is not only made of stock options. Other financial products enable market actors to target specific aims. For example, an oil buyer like a flight company may want to cover the risk of increase in the price of oil. In this case it is possible to buy on the financial market what is known as a "Call" or a "Call Option".
A Call Option is a contract between two counterparties (the flight company and a financial actor). The buyer of the Call has the opportunity but not the obligation to buy a certain quantity of a certain product (called the underlying) at a certain date (the maturity) for a certain price (the strike).
A Call Option is a contract between two counterparties (the flight company and a financial actor). The buyer of the Call has the opportunity but not the obligation to buy a certain quantity of a certain product (called the underlying) at a certain date (the maturity) for a certain price (the strike).
Feb
17
Temporal network model - Barabási-Albert model with the library igraph
I found a golden website. The blog of Esteban Moro. He uses R to work on networks. In particular he has done a really nice code to make some great videos of networks. This post is purely a copy of his code. I just changed a few arguments to change colors and to do my own network.
To create the network, I used the Barabási-Albert algorithm that you can find at the end of the post on the different algorithms for networks. Igraph is the library which has been used.
To create the network, I used the Barabási-Albert algorithm that you can find at the end of the post on the different algorithms for networks. Igraph is the library which has been used.
Dec
12
How to plot a network (package network) - Tip 2
As you have certainly seen now, I like working on artificial neural networks. I have written a few posts about models with neural networks (Models to generate networks, Want to win to Guess Who and Study of spatial segregation).
Unfortunately, I missed so far a nice and pleasant aspect of networks : its graphical approach. Indeed, plots of neural networks are often really nice and really useful to understand the network.
Sometimes such a graph can point out some characteristics of the network.
Unfortunately, I missed so far a nice and pleasant aspect of networks : its graphical approach. Indeed, plots of neural networks are often really nice and really useful to understand the network.
Sometimes such a graph can point out some characteristics of the network.
Dec
4
Have you tried to understand your network? - Random generation of network models
I already talked about networks a few times in this blog. In particular, I had this approach to explain spatial segregation in a city or to solve the Guess Who? problem. However, one of the question is how to generate a good network. Indeed, I aim to study strategy to split a network, but I need first to work with a realistic neural network. I could have downloaded data of a network, but I'd rather study the different models proposed to generate neural networks.
Nov
19
Function apply() - Tip 1
The function apply() is certainly one of the most useful function. I was scared of it during a while and refused to use it. But it makes the code so much faster to write and so efficient that we can't afford not using it. If you are like me, that you refuse to use apply because it is scary, read the following lines, it will help you. You want to know how to use apply() in general, with a home-made function or with several parameters ? Then, go to see the following examples.
Nov
15
Want to win "Guess who?" - Have an institutional neural network approach
Have you ever played the board game "Guess who?". For those who have not experienced childhood (because it might be the only reason to ignore this board game), this is a game consisting in trying to guess who the opponent player is thinking of among a list of characters - we will call the one he chooses the "chosen character". These characters have several characteristics such as gender, having brown hair or wearing glasses.
Nov
14
How to choose your next holidays destination - Uniform distribution on a sphere
If you want to choose randomly your next holidays destination, you are likely to process in a way which is certainly biased. Especially if you choose randomly the latitude and the longitude. A bit like they do in this lovely advertising (For those of you who do not speak French, this is about a couple who have won the national gamble prize and have to decide their next travel. The husband randomly picks Australia and the wife is complaining : "Not again!").
Nov
3
Generation of a normal distribution from "scratch" - The box-muller method
My previous post is about a method to simulate a Brownian motion. A friend of mine emailed me yesterday to tell me that this is useless if we do not know how to simulate a normally distributed variable.
My first remark is: use the rnorm() function if the quality of your simulation is not too important (Later, I'll try to explain you why the R "default random generation" functions are not perfect). However, it may be fun to generate a normal distribution from a simple uniform distribution.
My first remark is: use the rnorm() function if the quality of your simulation is not too important (Later, I'll try to explain you why the R "default random generation" functions are not perfect). However, it may be fun to generate a normal distribution from a simple uniform distribution.
Oct
29
Generate stock option prices - How to simulate a Brownian motion
The Brownian motion is certainly the most famous stochastic process (a random variable evolving in the time). It has been the first way to model a stock option price (Louis Bachelier's thesis in 1900).
The reason why is easy to understand, a Brownian motion is graphically very similar to the historical price of a stock option.
The reason why is easy to understand, a Brownian motion is graphically very similar to the historical price of a stock option.
Oct
17
The consequence of merging insurance companies - Risk simulation and probability of ruin
The merge of two insurance companies enables to curb the probability of ruin by sharing the risk and the capital of the two companies.
For example, we can consider two insurance companies, A and B. A is a well known insurance company with a big capital and is dealing with a risk with a low variance. We will assume that the global risk of all its customers follow a chi-square distribution with one degree of freedom.
For example, we can consider two insurance companies, A and B. A is a well known insurance company with a big capital and is dealing with a risk with a low variance. We will assume that the global risk of all its customers follow a chi-square distribution with one degree of freedom.
Add a comment