KDE 4.8 finally has a dock

KDE 4.8 finally has a dock: you just have to add the plasma icon tasks. Also the flexibility around ALT+TAB is welcome. With Krusader as file manager, Thunderbird and Firefox for email and web, it is becoming a real nice desktop, but it took a while since the very bad KDE 4.0 release.

It is easy to install under ubuntu 11.10 through the backports and seems very stable so far.

Something quite important is to tweak the fonts: use Déjà Vu Sans instead of Ubuntu fonts, use RGB subpixel rendering, use Crisp desktop effects. With those settings, KDE looks very nice. It's sad that they are not default in Kubuntu.

Update March 2013: It's been a while now that it is in the standard Ubuntu repositories and I believe installed by default, one has just to remove the task manager widget add the icon task widget:

One can also change the settings using a right click (I find useful not to highlight the windows) and it can look like:

KDE 4.8 finally has a dock

KDE 4.8 finally has a dock: you just have to add the plasma icon tasks. Also the flexibility around ALT+TAB is welcome. With Krusader as file manager, Thunderbird and Firefox for email and web, it is becoming a real nice desktop, but it took a while since the very bad KDE 4.0 release.

It is easy to install under ubuntu 11.10 through the backports and seems very stable so far.

Something quite important is to tweak the fonts: use Déjà Vu Sans instead of Ubuntu fonts, use RGB subpixel rendering, use Crisp desktop effects. With those settings, KDE looks very nice. It's sad that they are not default in Kubuntu.

Update March 2013: It's been a while now that it is in the standard Ubuntu repositories and I believe installed by default, one has just to remove the task manager widget add the icon task widget:

One can also change the settings using a right click (I find useful not to highlight the windows) and it can look like:

List of companies where I have been an employee

Intern:
Siemens (Berlin)
IBM (Boeblingen)
Osram Sylvania (Beverly, MA)

Employee:
Cap Gemini (Paris) working for Alcatel
Silicomp (Paris) working for Alcatel Nextenso
C2labs / one 0 development (San Francisco, CA) working for Whenmobile, Sony Pictures, GoPix, Technorati.
Credit Agricole alternative (Paris)
Prima solutions (Paris)
Esearchvision (Paris)
Ulink (Paris)
Edifixio (Paris)
Horizon (Paris)
Darty (Paris)
Calypso (Paris)

List of companies where I have been an employee

Intern:
Siemens (Berlin)
IBM (Boeblingen)
Osram Sylvania (Beverly, MA)

Employee:
Cap Gemini (Paris) working for Alcatel
Silicomp (Paris) working for Alcatel Nextenso
C2labs / one 0 development (San Francisco, CA) working for Whenmobile, Sony Pictures, GoPix, Technorati.
Credit Agricole alternative (Paris)
Prima solutions (Paris)
Esearchvision (Paris)
Ulink (Paris)
Edifixio (Paris)
Horizon (Paris)
Darty (Paris)
Calypso (Paris)

Generating random numbers following a given discrete probability distribution

I have never really thought very much about generating random numbers according to a precise discrete distribution, for example to simulate an unfair dice. In finance, we are generally interested in continuous distributions, where there is typically 2 ways: the inverse transform (usually computed in a numerical way), and the acceptance-rejection method (typically the ziggurat). The inverse transform is often preferred, because it's usable method for Quasi Monte-Carlo simulations while the acceptance rejection is not.

I would have thought about the simple way to generate random numbers according to a discrete distribution as first described here. But establishing a link with Huffman encoding is brilliant. Some better performing alternative (unrelated to Huffman) is offered there.

Generating random numbers following a given discrete probability distribution

I have never really thought very much about generating random numbers according to a precise discrete distribution, for example to simulate an unfair dice. In finance, we are generally interested in continuous distributions, where there is typically 2 ways: the inverse transform (usually computed in a numerical way), and the acceptance-rejection method (typically the ziggurat). The inverse transform is often preferred, because it's usable method for Quasi Monte-Carlo simulations while the acceptance rejection is not.

I would have thought about the simple way to generate random numbers according to a discrete distribution as first described here. But establishing a link with Huffman encoding is brilliant. Some better performing alternative (unrelated to Huffman) is offered there.