If you don’t know it yet, you will probably find out soon enough that I am a huge fan of Mars, the planet not the candy bar. This is a great picture of all the missions to Mars which I learned about through The Big Picture blog.

Here is another example of a possible dataset for analysis in my PSTAT 262 course this winter. For more details about the project, see here. Unfortunately I was not able to easily incorporate the picture into this post, so you will have to visit the figure at the Calculated Risk blog.

Having just moved to the state of California and having previously lived in a state where Jesse “the Body” Ventura used to be governor, I am severely disappointed with our governor and this veto letter. Perhaps, just maybe, the governor would have more cooperation with the legislature if he didn’t treat them with such disrespect.

Andrew Gelman over at his Statistical Modeling, Causal Inference, and Social Science blog often brings up figures that he thinks are good, bad, and ugly. I would like to continue that tradition here. I saw the figure below on Barry Ritholtz’s The Big Picture blog. Now, Barry’s blog has nothing to do with statistics or swine flu, but since everybody is interested in swine flu these days, he decided to put this figure on his blog. From a probabilistic perspective, the point of the figure is very clear, conditional on having the disease what is the probability of dying from it. I would be very interested in this quantity if I had the disease, but since I don’t have any of these diseases (that I am aware of), I don’t care. The quantity I’m interested in is my marginal probability of dying from the disease. (Certainly I would prefer a conditional probability where we condition on my age, gender, geographical location, etc, but that is asking a bit much). So although this is an interesting and colorful figure, it has no relevance (yet) to my situation, although I will be happy to know that my chances of surviving swine flu once I get it are pretty good.

Separately, why is the subtitle “Average % of infected who die”? Shouldn’t it just be `% of infected who die’?

Previously I discussed the Quantitative Methods in Social Sciences program here at UCSB. I am now a core faculty member of this program as can be seen here. This means I can serve as a QMSS advisor, although students can probably get more information by visiting the QMSS curriculum page. I can also be the outside representative on a QMSS student’s dissertation committee which is a requirement for the QMSS program. So I hope students start knocking down my door, although I’ll be happy if one shows up.

Previously I have mentioned a plan to have students analyze a data set.  Here is an interesting dataset concerning housing starts. From wikipedia, housing starts “refers to the number of privately owned new homes (technically housing units) on which construction has been started in a given period.” This index gives an indication of how the housing market is doing.

I’m currently attending Bioengineering Insights 2009. It is a one-day conference on the interdisciplinary research effort at UCSB and with industry in the fields of biology, engineering, chemistry, physics and (with any luck) statistics. The opening keynote address was given by Lee Hood of the Institute for Systems Biology. He gave an overview talk about the work his group is doing at ISB and where he believes medicine is headed in the future. In particular, promoted the idea of P4 medicine, where the P’s stand for Predictive, Preventive, Personalized, and Participatory and more information can be found here.

I attended Session A: Systems Biology this morning and learned alot about research going on at UCSB. The speakers were Linda Petzold, Cherie Briggs, Jean Carlson, Megan Valentine, and David Low. In particular, I think my research would mesh well with Linda Petzold and her work on Post-traumatic stress disorder, Cherie Briggs and her SIR-type modeling of fungal infections in frogs, or David Low and his Markov chain approach to understanding a 1kilobase pair region of the bacterial genome. Overall I thought Session A was full of exciting new research topics.

I will be giving a seminar today in my department entitled `A sequential Monte Carlo primer.’ The idea is to give those who are unfamiliar with the field an introduction. More can be read here and obviously can be heard by coming to South Hall 5607F @ 3:15 PM, refreshments available at 3PM.

Ever since watching An inconvenient truth, I have wondered about this figure that shows an apparent relationship between carbon dioxide and global temperature.  I couldn’t find a better version of this picture.

This is a pretty cool figure.