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Sets of single nucleotide polymorphisms (SNPs), or haplotypes, are widely used in the analysis of relationship between genetics and diseases. Due to the cost of obtaining exact haplotype pairs, genotypes which contain the unphased information corresponding to the haplotype pairs in the test subjects are used. Various haplotype inference algorithms have been proposed to resolve the unphased information. However, most existing algorithms are limited in different ways. For statistical algorithms, the limiting factors are often in terms of the number of SNPs allowed in the genotypes, or the number of subjects in the dataset. In this paper, we propose a deterministic sequential Monte Carlo-based haplotype inference algorithm which allows for larger datasets in terms of number of SNPs and number of subjects, while providing similar or better performance for datasets under various conditions.