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The Inferentialist's Blog

This is a blog about my day to day work as a statistical consultant.

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The Inferentialist's Blog | blog.inferentialist.com Reviews

https://blog.inferentialist.com

This is a blog about my day to day work as a statistical consultant.

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1

[C] Dynamic Programming 101: Change for a Dollar

https://blog.inferentialist.com/2016/10/06/change-for-a-dollar.html

C] Dynamic Programming 101: Change for a Dollar. Oct 6, 2016. We describe an efficient dynamic programming algorithm to compute the number of ways that one might make change for a dollar. The answer, assuming pennies, nickles, dimes, quarters, and half-dollars? Two hundred ninety two. Change for a Dollar. This defines the auxiliary variables and computes increasingly larger-value solutions. Why write in C? Created on: Oct 6, 2016. Include coin vec.h. Cv tree node to soln. Cv tree node t. Cv tree node t.

2

[R] Adaptive Rejection Sampling

https://blog.inferentialist.com/2016/09/26/adaptive-sampling.html

R] Adaptive Rejection Sampling. Sep 26, 2016. Adaptive rejection sampling is a statistical algorithm for generating samples from a univariate, log-concave density. Because of the adaptive nature of the algorithm, rejection rates are often very low. The exposition of this algorithm follows the example given in Davison’s 2008 text, Statistical Models. The algorithm is fairly simple to describe:. Let Because of the piecewise-linear construction of , is piecewise-exponential, sampling is straightforward.

3

Multivariate Normal: Conditional Density Derivation

https://blog.inferentialist.com/2016/09/19/mvn-conditional-distribution.html

Multivariate Normal: Conditional Density Derivation. Sep 19, 2016. We derive the classical result: what is the density of a multivariate normal conditioned on some proper subset of its components? That is, if. Then we want to characterize. We’ll want a couple of preliminary results before establishing the primary result. Using the moment generating function, it is easy to show that the marginal distribution of is. Then the quadratic term in is. In particular, we can write the conditional density as.

4

[Python] Random Access Priority Queue

https://blog.inferentialist.com/2016/10/05/random-access-priority-queue.html

Python] Random Access Priority Queue. Oct 5, 2016. We describe a priority queue data structure that allows item removal via key or removal via (lowest) priority. As a bonus, there is code that shows how to implement a wrapper for a decorator class that enables per-method parameters and has access to class variables. Random Access Priority Queue. The RandomAccessPriorityQueue class pairs a heap with a dictionary and provides O(log(n) insertion and removal. Must be a subclass of PriorityItem. Invariant: mo...

5

[Python] Pandas.DataFrame, PostgreSQL, and Autoincrementing Columns

https://blog.inferentialist.com/2016/12/04/serialized-sql-pandas.html

Python] Pandas.DataFrame, PostgreSQL, and Autoincrementing Columns. Dec 4, 2016. Pandas.DataFrame has a. Convenience method for pumping dataframes to SQL tables. However, there is no parameterization of. That will create an autoincrementing column index. This post details a simple workaround. Create an Autoincrementing Column. The key observation is that sqlalchemy supports the creation of a SERIAL type and that this functionality is exposed through pandas, just not via the. Set up the database connection.

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Inferentialist LLC - lending club: loan originations

https://www.inferentialist.com/lendingclub/origination_map

Lending Club Loan Originations. The interactive figure shows Lending Club, peer-to-peer, loan originations through November 2013. Click on a state to deep dive into geographic segments. Inferentialist, LLC 2015.

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Inferentialist LLC - index

https://www.inferentialist.com/index

To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. Optimal Lending Club Portfolios. The optimal portfolio strategy has an annual return of above 12%. This constitutes a 30% to 50% improvement over a strategy based only on pre-assigned loan grades. Sparse Functional Models: Predicting Crop Yields. In this case, a simplest model might estimate the per-county yield by the average, ta...

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Inferentialist LLC - lending club: markov chain models

https://www.inferentialist.com/lendingclub/markov

Lending Club Loan Quality Metrics. Lending Club releases compiled daily loan data to disclose risk metrics to potential investors. Here, we provide a few summary statistics, based on Markov chain models, that describe loan state transitions. We are interested in understanding, for example, how often a loan transitions from a late payment to a default. This table shows the marginal distribution of loan states for all loans, past and present. Loan Status Distribution as of August 25, 2014 02:18. The 'Defau...

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Inferentialist LLC - white papers

https://www.inferentialist.com/resources/white_papers

Sparse Functional Models: Predicting Crop Yields. This paper develops an estimator for Iowa soybean yield based on daily precipitation data. We construct the estimator as an averaging kernel of an integral transform and exploit the inherent sparity in our representation. Optimal Lending Club Portfolios. On Lending Club Portfolios. This short paper examines default risk in the context of historical investment returns obtained through the Lending Club platform. Color Vision: The Inferentialist Logo.

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Inferentialist LLC - maps: us census data

https://www.inferentialist.com/map/msamain

Metropolitan Statistical Areas Heatmaps. No High School Diploma. Bachelor's Degree or Higher. Inferentialist, LLC 2015.

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Inferentialist LLC - msa geography mappings

https://www.inferentialist.com/map/tools

Metropolitan Statistical Areas Data Manipulation. Metropolitan statistical areas (MSA) are well defined areal regions used by many government datasets. However, in the context of building a statistical model, they can be difficult to work with. Here, we provide simplified polygon geometries as well as a number of files that facilitate mappings from zipcode, city, and state into MSAs. CBSAFP NLINES # a comment line containing metadata LONG LAT # NLINES of latlong point data associated with the CBSAFP.

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