Dirichlet Posterior Sampling with Truncated Multinomial Likelihoods

We consider the problem of drawing samples from posterior distributions formed under a Dirichlet prior and a truncated multinomial likelihood, by which we mean a Multinomial likelihood function where we condition on one or more counts being zero a priori. Sampling this posterior distribution is of interest in inference algorithms for hierarchical Bayesian models based on the Dirichlet distribution or the Dirichlet process, particularly Gibbs sampling algorithms for the Hierarchical Dirichlet Process Hidden Semi-Markov Model. We provide a data augmentation sampling algorithm that is easy to implement, fast both to mix and to execute, and easily scalable to many dimensions. We demonstrate the algorithm's advantages over a generic Metropolis-Hastings sampling algorithm in several numerical experiments

Determinants of Financial Performance of Commercial Dairy Farms.

Data from the 1993 Farm Costs and Returns Survey were used in a multi-variate analysis framework to determine factors associated with the financial performance of commercial dairy farm operations. Statistical equivalency tests revealed regional differences in the way extensive indebtedness, size of operation, and labor cost affect net farm incomes. Regional differences were also found in terms of how milk production per cow, per-unit cost of purchased feed, and level of adoption of capital intensive technologies affect per-unit returns. Examination of the variation in the net farm income of commercial dairy farms using the method of coefficients of separate determination identified the size of the operation, regardless of the location of the farm business, as the factor contributing the most to the variability in net farm income. On a per-unit-of-returns basis, factors found most important in explaining the variation in net returns per hundredweight of milk sold were cow's productivity, and per-cow forage production and purchased feed costs.financial performance, net farm income, technological adoption, Lorenz curve, Gini coefficient, Agricultural Finance, Livestock Production/Industries,

An Analysis of Error Reconciliation Protocols for use in Quantum Key Distribution

Quantum Key Distribution (QKD) is a method for transmitting a cryptographic key between a sender and receiver in a theoretically unconditionally secure way. Unfortunately, the present state of technology prohibits the flawless quantum transmission required to make QKD a reality. For this reason, error reconciliation protocols have been developed which preserve security while allowing a sender and receiver to reconcile the errors in their respective keys. The most famous of these protocols is Brassard and Salvail\u27s Cascade, which is effective, but suffers from a high communication complexity and therefore results in low throughput. Another popular option is Buttler\u27s Winnow protocol, which reduces the communication complexity over Cascade, but has the added detriment of introducing errors, and has been shown to be less effective than Cascade. Finally, Gallager\u27s Low Density Parity Check (LDPC) codes have recently been shown to reconcile errors at rates higher than those of Cascade and Winnow with a large reduction in communication, but with greater computational complexity. This research seeks to evaluate the effectiveness of these LDPC codes in a QKD setting, while comparing real-world parameters such as runtime, throughput and communication complexity empirically with the well-known Cascade and Winnow algorithms. Additionally, the effects of inaccurate error estimation, non-uniform error distribution and varying key length on all three protocols are evaluated for identical input key strings. Analyses are performed on the results in order to characterize the performance of all three protocols and determine the strengths and weaknesses of each

The Accountable Attorney: A Proposal to Revamp the Aba’s 1976 Statement of Policy Regarding Lawyers’ Responses to Auditors’ Requests for Information

The American Bar Association\u27s (ABA) long-standing compromise\u27 with the American Institute of Certified Public Accountants (AICPA) regarding the role of lawyers in audits of public companies needs an overhaul. The current process is dysfunctional: accountants attempt to opine on a company\u27s disclosures about litigation and to assess loss exposure without any foundation in the legal issues involved. During this process, those accountants turn to their clients\u27 attorneys for help. But lawyers, concerned about preserving confidentiality and privilege, routinely respond to the accountants\u27 requests with verbiage honed to say nothing at all. This Comment argues that the time has come to divorce CPAs from the task of evaluating legal risks in the course of their audits. Instead, an Independent Legal Counsel should report on the company\u27s disclosure of legal matters. The benefits of this approach are: (1) to eliminate ineffective and unproductive handoffs under the current audit processes; (2) to avoid troubling attorney-client privilege risks during the background discussions of the legal contingencies; and (3) to improve public confidence in the legal disclosures made in financial statements

SUCCESSION IN FAMILY FARM BUSINESS: EMPIRICAL EVIDENCE FROM THE U.S. FARM SECTOR

Survival of many family farms is dependent on successful intergenerational transfer. Given the importance of succession in the farm sector, the purpose of this paper is to examine factors that are likely to influence succession decisions on U.S. farms. The paper uses 2001 ARMS data and a multinomial Logit (MNL) regression to estimate family succession, non-family succession, and farm exit decisions of farm households in the U.S. Model choice and specification issues are discussed. Results indicate that operator's education, household wealth, growth in farm size, and farm debt are important factors that determine succession decisions. Additionally, farm specialization is taken into consideration when farm operators make their succession plans.Farm Management,

SUCCESSION DECISIONS AND RETIREMENT INCOME OF FARM HOUSEHOLDS

Consumer/Household Economics,

The Hierarchical Dirichlet Process Hidden Semi-Markov Model

There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the traditional HMM. However, in many settings the HDP-HMM's strict Markovian constraints are undesirable, particularly if we wish to learn or encode non-geometric state durations. We can extend the HDP-HMM to capture such structure by drawing upon explicit-duration semi- Markovianity, which has been developed in the parametric setting to allow construction of highly interpretable models that admit natural prior information on state durations. In this paper we introduce the explicitduration HDP-HSMM and develop posterior sampling algorithms for efficient inference in both the direct-assignment and weak-limit approximation settings. We demonstrate the utility of the model and our inference methods on synthetic data as well as experiments on a speaker diarization problem and an example of learning the patterns in Morse code

Mineralogy and Geochemistry of Shergottite RBT 04262

This abstract presents mineralogical and geochemical data on newly discovered shergottite RBT 04262 and compares it with other known shergottites

Simplifying Satellite and Ground Data Validation with Level-2 Subsetting

We demonstrate that scientists can simplify their satellite data validation workflow with the use of NASA Godddard Earth Sciences Data and Information Services Center (GES DISC) subsetting services. We perform a sample validation of Aura ozone products collocated with ground-based ozone measurements using subsetting services to trim satellite data to only the relevant user-defined variables and spatio-temporal region. Because the subsetting service automatically returns only relevant data granules that adhere to a set of user-defined coincidence criteria, user workload is greatly reduced. Moreover, the resultant data files are substantially smaller than full data granules due to the subsetting service further culling the data to the relevant geospatio-temporal coincidence criteria, user-defined variables, and user-defined dimensions of variables. This decreases data download throughput and file storage requirements. The validation presented here quantifies the time and file size savings that can be achieved by utilizing subsetting services within the satellite data validation workflow