Rare event simulation for stochastics networks

Duration: 57 mins 26 secs

Share this media item:

Embed this media item:

<iframe width="_width_" height="_height_" src="https://sms.cam.ac.uk/media/859235/embed" frameborder="0" scrolling="no" allowfullscreen></iframe>

Choose size:

About this item

Available Formats

About this item

Description:	Blanchet, J (Columbia) Monday 21 June 2010, 09:10-10:00

Created:

2010-06-24 17:30

Collection:

Stochastic Processes in Communication Sciences

Publisher:

Isaac Newton Institute

Blanchet, J

Language:

eng (English)

Credits:

Author:

Blanchet, J


Abstract:	We shall discuss several importance sampling techniques for efficient estimation of overfow probabilities or sampling conditional paths in stochastic networks. We are interested in a wide class of environments including light-tailed and heavy-tailed networks. Our discussion includes algorithms that are optimal in the sense of achieving the best possible rate of convergence as the associated large deviations parameter increases. For instance, in the case of arbitrary Jackson networks our estimators are strongly efficient and we can generate exact conditional overflow paths in linear time (as a function of the overflow level). This talk is based on joint work with Peter Glynn, Henry Lam, Kevin Leder, Chenxin Li and Jingchen Liu.

Abstract:

We shall discuss several importance sampling techniques for efficient estimation of overfow probabilities or sampling conditional paths in stochastic networks. We are interested in a wide class of environments including light-tailed and heavy-tailed networks. Our discussion includes algorithms that are optimal in the sense of achieving the best possible rate of convergence as the associated large deviations parameter increases. For instance, in the case of arbitrary Jackson networks our estimators are strongly efficient and we can generate exact conditional overflow paths in linear time (as a function of the overflow level). This talk is based on joint work with Peter Glynn, Henry Lam, Kevin Leder, Chenxin Li and Jingchen Liu.

Available Formats

Format	Quality	Bitrate	Size
MPEG-4 Video	480x360	1.84 Mbits/sec	794.16 MB	View	Download
WebM	480x360	379.62 kbits/sec	159.69 MB	View	Download
Flash Video	480x360	797.91 kbits/sec	336.13 MB	View	Download
iPod Video	480x360	505.35 kbits/sec	212.89 MB	View	Download
QuickTime	384x288	848.67 kbits/sec	357.52 MB	View	Download
MP3	44100 Hz	125.03 kbits/sec	52.46 MB	Listen	Download
Windows Media Video		478.0 kbits/sec	201.37 MB	View	Download
Auto *	(Allows browser to choose a format it supports)