### NBA Predictions for 11/21/2012

h_str = home team strength (including home court advantage)
o_str = opponent team strength (including away court disadvantage)
pr_home = estimated probability of home team winning

home | opp | h_str | o_str | pr_home
------+-----+-------+-------+---------
ATL  | WAS |  1.03 |  0.93 |    0.85
BOS  | SAS |  1.00 |  1.03 |    0.40
CHA  | TOR |  0.99 |  0.96 |    0.63
CLE  | PHI |  0.98 |  0.98 |    0.48
DAL  | NYK |  1.02 |  1.08 |    0.28
GSW  | BRK |  1.01 |  1.00 |    0.56
HOU  | CHI |  1.02 |  0.97 |    0.70
IND  | NOH |  1.01 |  0.95 |    0.72
MIA  | MIL |  1.07 |  0.99 |    0.78
MIN  | DEN |  1.01 |  0.99 |    0.58
OKC  | LAC |  1.05 |  1.06 |    0.49
ORL  | DET |  0.97 |  0.95 |    0.56
PHO  | POR |  0.97 |  0.97 |    0.52
SAC  | LAL |  0.95 |  1.01 |    0.28

### A Bayes' Solution to Monty Hall

For any problem involving conditional probabilities one of your greatest allies is Bayes' Theorem. Bayes' Theorem says that for two events A and B, the probability of A given B is related to the probability of B given A in a specific way.

Standard notation:

probability of A given B is written $$\Pr(A \mid B)$$
probability of B is written $$\Pr(B)$$

Bayes' Theorem:

Using the notation above, Bayes' Theorem can be written: $\Pr(A \mid B) = \frac{\Pr(B \mid A)\times \Pr(A)}{\Pr(B)}$Let's apply Bayes' Theorem to the Monty Hall problem. If you recall, we're told that behind three doors there are two goats and one car, all randomly placed. We initially choose a door, and then Monty, who knows what's behind the doors, always shows us a goat behind one of the remaining doors. He can always do this as there are two goats; if we chose the car initially, Monty picks one of the two doors with a goat behind it at random.

Assume we pick Door 1 and then Monty sho…

### Notes on Setting up a Titan V under Ubuntu 17.04

I recently purchased a Titan V GPU to use for machine and deep learning, and in the process of installing the latest Nvidia driver's hosed my Ubuntu 16.04 install. I was overdue for a fresh install of Linux, anyway, so I decided to upgrade some of my drives at the same time. Here are some of my notes for the process I went through to get the Titan V working perfectly with TensorFlow 1.5 under Ubuntu 17.04.

Old install:
Ubuntu 16.04
EVGA GeForce GTX Titan SuperClocked 6GB
2TB Seagate NAS HDD

New install:
Ubuntu 17.04
Titan V 12GB
/ partition on a 250GB Samsung 840 Pro SSD (had an extra around)
/home partition on a new 1TB Crucial MX500 SSD
New WD Blue 4TB HDD