Comments regarding Henry Templeman's T-Model:

"I truly believe that we can individualize fingerprints to a donor as long as we respect certain principles and rules. Your model can certainly assist in generating good outcomes and underpinning results.  What I like about it is that it seems an integrated solution because it incorporates the expert knowledge, guides the analysis and comparison and assists in making the findings and results more objective."

"I like the idea of assigning weight and value to different types of minutiae and value of clarity and similarity."

"Your model has the advantage over other models that it establishes the weight/value of a mark on itself by calculating the chance of existence of a look alike. In general this is much better understood by experts and the receivers of the evidence."

"I honestly believe your model could assist us in making things better and more transparent."

Arie Zeelenberg

Senior Fingerprint Advisor

National Police Force of the Netherlands

3/7/2010

"I stumbled across your web site and after review came away very impressed with the quality of work and the direction you are headed.  I'm currently the lab director of the Contra Costa County Sheriff's Forensic Services Division and oversee both a Latent Print Unit and ID Unit along with other forensic disciplines."

"Your likelihood model rings true to me being a former DNA analyst."

"In my opinion, the work you have done has set the right foundation to build on, especially after the NAS Report."

Paul E. Holes

Chief Forensic Services Division

Office of the Sheriff

Contra Costa County

5/18/2009

"You have a lot of information of which I would like my own staff to be aware.  I am impressed with your use of the T-Model.  This is an example that I believe in and would very much like to see developed and embraced by the Latent Community."

Roy Marzioli, Manager

Central Identification Services

Forensic Services Division

Contra Costa County Office of the Sheriff

5/19/2009

“A major work like this is long overdue to address the issue of the old dogma, black or white, absolute or can’t say a thing.”, and “An open and widespread conversation on this topic is long overdue in our field.” 

“The ridge unit approach to illustrate a ridge is fine.” 

 
“There are some really strong ideas here.  I also think that you are joining a growing group of examiners that are thinking outside the box and recognizing the need to appropriately weight the corresponding features.  I like the initiative of this.” 

“You are approaching this from a frequentist point of view, rather that Bayesian—which is fine—but changes the framework of the propositions and can lead to a few problems, but these can be avoided.” 

“I am not a statistician and cannot comment on your formulas but your logic is sound.”

and,

“Great work on a needed sufficiency research and robust probabilistic model.” 

“Quite interesting, have to admit you seem to have done a lot of research.  I can follow most all of your reasoning and while I will admit to not grasping all of its contents, it seems a favorable piece of work.” 

“I have read through several of your later revisions and thought it was really well written and based on sound science and statistical computation/theory.” 

Excerpts from Super Crunchers by Ian Ayres [74]:

"First and foremost, Super Crunchers (e.g. statistical models) are better at making predictions because they do a better job at figuring out what weights should be put on individual factors in making a prediction.  Indeed, regression equations are so much better than humans at figuring out appropriate weights that even very crude regressions with just a few variables have been found to outperform humans."

"Unlike self-involved experts, statistical regressions don't have egos or feelings....Statistical predictions are also not overconfident."

"Decisions that are backed by quantitative prediction are at least as good as and often substantially better than decisions based on mere lived experience.  The mounting evidence of statistical superiority has led many to suggest that we should strip experts of at least some of their decisionmaking authority."

"In context after context, decision makers who wave off the statistical predictions tend to make poorer decisions."

continued... 

"Humans do make better predictions when they are provided with the results of statistical prediction.  The problem is that even with Super Crunching assistance (e.g. statistical modeling), humans don't predict as well as the Super Crunching prediction by itself....so the experts get better if you give them the model.  But still the model by itself performs better."

"The most important thing that is left to humans is to use their minds and intuitions to guess at what variables should and should not be included in statistical analysis.  A statistical regression can tell us the weights to place upon various factors....humans, however, are crucially needed to generate the hypotheses about what causes what."

"In the new world of database decision making, [these] assessments are merely inputs for a formula and it is statistics, and not experts, which determine how much weight is placed on the assessments."

An introduction to Ian Ayres work is presented on YouTube.