New York Investment Network



Pitching Help Desk


Testimonials

"For those of you that are asking yourself whether this site is real, the answer is yes. My first thought was that I would put my proposal on the site and it would be sent for review, and at this point someone from within the Dealflow Investment Network office would contact me as an investor so I would be more likely to pay the $249 fee. I received 8 responses from investors overnight and 2 more since then. Thanks Dealflow Investment Network."
David Kriedeman - Chris Christopherson Inc

 BLOG >> Recent

Estimating Probability Distributions: Part 2 [Statistics
Posted on July 18, 2013 @ 04:17:00 PM by Paul Meagher

In my last blog, I discussed some useful probability distributions for representing our uncertainty about a parameter; the uniform and the triangular distributions.

Our uncertainty about a parameter θ such as "the price of gas next week" can be represented using a uniform distribution where the gas price could be anywhere between some low estimate and some high estimate of the price next week. If we also want to hazard a guess as to the most likely value, then we would be using a triangular distribution to represent our uncertainty about the price of gas.

There are other simple techniques for eliciting a probability distribution to represent our uncertainty about a parameter. In today's blog I want to discuss a simple technique called "Merit Scoring".

The Future Price of Corn

The easiest way to explain this technique is if you look at the table below.

Corn Price (per bushel)Merit Score
$4.25?
$4.50?
$4.75?
$5.00?
$5.25?

The table has future corn prices ranging from $4.50 to $5.50 per bushel (see quotecorn.com for current price). Now, I might ask you to assign a relative merit score to each price point in this range. A merit score can range between, say, 1 and 10. If you assign a merit score of 1 to a price point, that means you think the future price will not be nearest to that price - the price estimate has low merit. Conversely, a merit score of 10 means that you think the future price will be nearest to that price - the price estimate has high merit. My merit scores for the price of corn on Sept 1, 2013, looks like this.

Corn Price (per bushel)Merit Score
$4.251
$4.505
$4.7510
$5.008
$5.253

In this example, we are not directly assigning a probability to each possible price point. Instead we are supplying a merit score to each possible price point. We can easily convert each merit score to a corresponding probability by summing all the merit scores and then dividing each merit score by this sum. The result is a probability assignment for each price point with probabilities for each price point summing to 1. This gives as a probability distribution for our parameter which is the price of corn on Sept 1, 2013.

To demonstrate how merit scores can be converted to probabilities and how this forms a probability distribution I have devised a PHP-based script that shows how the calculation is done, what the calculated price probabilities are, and that these probabilities sum to 1.

Conclusions

The merit scoring technique and script can be used to estimate a probability distribution for any parameter that interests you. One limitation of this technique is that it is discrete in nature so can't give you probabilties for prices that might fall between two price points (e.g., $4.85). This may be of concern if you think you should be trying to estimate the future price of corn with more resolution (e.g., 10 cent increments) and/or the daily variability in corn prices is not that high. The daily price of corn is actually quite high so being correct to within 25 cents might be a good goal for your predictions.

Permalink 

 Archive 
 

Archive


 November 2020 [1]
 September 2020 [1]
 June 2020 [4]
 May 2020 [1]
 April 2020 [2]
 March 2020 [1]
 February 2020 [1]
 January 2020 [1]
 December 2019 [1]
 November 2019 [2]
 October 2019 [2]
 September 2019 [1]
 July 2019 [1]
 June 2019 [2]
 May 2019 [2]
 April 2019 [5]
 March 2019 [4]
 February 2019 [3]
 January 2019 [3]
 December 2018 [4]
 November 2018 [2]
 September 2018 [2]
 August 2018 [1]
 July 2018 [1]
 June 2018 [1]
 May 2018 [5]
 April 2018 [4]
 March 2018 [2]
 February 2018 [4]
 January 2018 [4]
 December 2017 [2]
 November 2017 [6]
 October 2017 [6]
 September 2017 [6]
 August 2017 [2]
 July 2017 [2]
 June 2017 [5]
 May 2017 [7]
 April 2017 [6]
 March 2017 [8]
 February 2017 [7]
 January 2017 [9]
 December 2016 [7]
 November 2016 [7]
 October 2016 [5]
 September 2016 [5]
 August 2016 [4]
 July 2016 [6]
 June 2016 [5]
 May 2016 [10]
 April 2016 [12]
 March 2016 [10]
 February 2016 [11]
 January 2016 [12]
 December 2015 [6]
 November 2015 [8]
 October 2015 [12]
 September 2015 [10]
 August 2015 [14]
 July 2015 [9]
 June 2015 [9]
 May 2015 [10]
 April 2015 [10]
 March 2015 [9]
 February 2015 [8]
 January 2015 [5]
 December 2014 [11]
 November 2014 [10]
 October 2014 [10]
 September 2014 [8]
 August 2014 [7]
 July 2014 [6]
 June 2014 [7]
 May 2014 [6]
 April 2014 [3]
 March 2014 [8]
 February 2014 [6]
 January 2014 [5]
 December 2013 [5]
 November 2013 [3]
 October 2013 [4]
 September 2013 [11]
 August 2013 [4]
 July 2013 [8]
 June 2013 [10]
 May 2013 [14]
 April 2013 [12]
 March 2013 [11]
 February 2013 [19]
 January 2013 [20]
 December 2012 [5]
 November 2012 [1]
 October 2012 [3]
 September 2012 [1]
 August 2012 [1]
 July 2012 [1]
 June 2012 [2]


Categories


 Agriculture [72]
 Bayesian Inference [14]
 Books [15]
 Business Models [24]
 Causal Inference [2]
 Creativity [7]
 Decision Making [15]
 Decision Trees [8]
 Design [37]
 Eco-Green [4]
 Economics [12]
 Education [10]
 Energy [0]
 Entrepreneurship [65]
 Events [2]
 Farming [20]
 Finance [25]
 Future [15]
 Growth [18]
 Investing [25]
 Lean Startup [10]
 Leisure [5]
 Lens Model [9]
 Making [1]
 Management [9]
 Motivation [3]
 Nature [22]
 Patents & Trademarks [1]
 Permaculture [36]
 Psychology [2]
 Real Estate [2]
 Robots [1]
 Selling [11]
 Site News [19]
 Startups [12]
 Statistics [3]
 Systems Thinking [3]
 Trends [7]
 Useful Links [3]
 Valuation [1]
 Venture Capital [5]
 Video [2]
 Writing [2]