Wheel of Scripture

Several years ago, I volunteered in Kids Ministry. We had to come up with original ways to keep them engaged and not too bored. Bored kids will eat you alive, even in church, lol! 

So anyway, I coded a basic "Wheel of Fortune" clone called "Wheel of Scripture". It helped them to memorize verses that had to do with our lesson. We had it up on the projector and made a kind of game show out of it.
Later my laptop crashed and I lost most of my projects. I found an old CD-Rom recently with the game on there and thought I'd share a screenshot of my cheesy game.

At the moment, there isn't much to it. The board is blank, save for the letter spaces. The game was pretty straight-forward: The players would simply guess a letter, and the operator, usually someone in the Media booth, would hit the key for that letter. If the letter was a correct guess, the game plays a *ding* sound and the letter appears in one of the blank spaces. If the guess is incorrect, it buzzes. The letter is crossed off at the bottom automatically, and the loop continues. 

There is no animation or music when the puzzle is solved. I may go back and finish the game at some point, and add that as a feature. Other ideas include:

-Adding scores

-Adding an actual wheel to spin, and include "Lose a Turn", "Vowel", "Spin Again" etc

-Adding more levels

-Adding the ability to "speak" the letter (language processing), so the game could work as a mobile game employing the microphone

This is one of those fun projects that kind of got abandoned. Game developers all have a huge graveyard of these, like little sketchbooks with incomplete drawings. Hopefully I'll be able to develop it into a fully playable game. Time will tell.

Bayesian Model Challenges

By Dax Bradley

Possible disadvantages to Bayesian learning methods can be significant.  For one thing, the information gleaned from the effort could be theoretically infeasible.  The act of specifying a prior datum is challenging.  Generally, it is necessary to specify a real number for every setting in the real-world parameters (Langford, 2005).  One potential solution is to acquire languages that allow more compact specification of priors.  The task of learning a new language may be prohibitive.  Another solution is to simply lie.  In other words, create a specification that is not accurate.  As deceitful as this sounds, it is not meant that way; in fact, it has been shown to be effective (Langford, 2005).

Another known disadvantage to Bayesian modeling lies in the analysis of causality.  There is a great challenge in analyzing the conditional probability links between various nodes.  Even with physical causal models and known variables, the computational burden is great, since the task equates to inverting a finite-element model (Sankararaman, 2015).  This is especially daunting if the analysis revolves around an acyclic model.  The assumption of independence among variables implies no latent cofounding variable in Bayesian modeling; however, often there are acyclic latent cofounding variables which can force the model to be seriously biased (Shimizu & Bollen, 2014).

Prior choice problems present challenges in the Bayesian methodology.  Another scenario in which researchers using the model is in astronomy.  Scientists have attempted to predict the number of extra-solar planets in this way.  Improper priors are generally disallowed here, so other strategies have been suggested, such as Intrinsic Bayes, Fractional Bayes, and Expected posterior factors (Jeffreys, 2006, p. 11).  The potential defeat of using such methods is that data may be inadvertently duplicated, so allowances must be made.

Many forms of industry employ the Bayesian model of statistical probability.  In drug development, the approach is widely regarded for its accuracy and natural interpretation.  It does, however, demand no small degree of planning.  The flexibility of this method creates an environment prone to potential computational errors.  Without proper planning, the various outcomes of intense computation may be unreliable, prompting some teams to opt instead for a frequentist model (Gupta, 2012).

References

Gupta, S. (2012, June). Use of Bayesian statistics in drug development: Advantages and challenges. International Journal of Applied & Basic Medical Research, 2(1), 3-6. http://dx.doi.org/Use of Bayesian statistics in drug development: Advantages and challenges

Jeffreys, W. (2006). Current challenges in Bayesian model choice: Comments [Lecture notes]. Retrieved from Department of Statistics University of Vermont: http://astrostatistics.psu.edu/scma4/Jefferys.pdf

Langford, J. (2005, April 23). Advantages and disadvantages to Bayesian learning [Discussion group comment]. Retrieved from http://hunch.net/?p=65
Sankararaman, S. (2015). What are the limitations of Bayesian Networks? Retrieved January 18, 2017, from https://www.quora.com/What-are-the-limitations-of-Bayesian-Networks

Shimizu, S., & Bollen, K. (2014, August). Bayesian Estimation of Causal Direction in Acyclic Structural Equation models with individual-specific confounder variables and non-Gaussian distributions. Journal of Machine Learning Research, 15(1), 2629-2652. Retrieved from http://jmlr.org/papers/volume15/shimizu14a/shimizu14a.pdf